Infection Control & Hospital Epidemiology

Importance: Clinicians often look for a dental source in Staphylococcus aureus bacteremia (SAB), but how often dental disease is found, compared with other bacteremias, is unclear. Objective: To compare documented dental disease in adults with SAB, Escherichia coli bacteremia, and a general population cohort. Design, Setting, and Participants: Retrospective cohort study in the Protege database of 48 975 adults with bacteremia from January 1, 2015, through December 31, 2023, assigned by earliest in-window organism, and 81 945 general population adults. Exposures: SAB, with E coli bacteremia as the primary comparator and the general population as the secondary comparator. Main Outcomes and Measures: Documented dental disease from 7 days before through 30 days after index, defined by a qualifying dental procedure or disease-specific note text and adjusted for age, sex, race, and documentation intensity. Results: Dental disease occurred in 528 of 16 435 patients with SAB (3.21%) and 179 of 24 942 patients with E coli bacteremia (0.72%). The adjusted odds ratio was 3.86 (95% CI, 3.23 to 4.63). By the pathology-only definition, disease occurred in 517 patients with SAB (3.15%) and 399 general population patients (0.49%). The adjusted odds ratio was 3.71 (95% CI, 3.08 to 4.46). Within SAB, rates were highest with prior infective endocarditis (9.8%), opioid use disorder (6.2%), injection drug use (5.3%), and prosthetic valve (5.0%). Prevalence ranged from 0.49% in the general population to 7.98% in other streptococcal bacteremia. Of 437 patients with SAB and an assignable first finding date, 333 (76.2%) had the finding after the index date. Conclusions and Relevance: Dental disease was documented more often in SAB than in E coli bacteremia after adjustment. Most findings were first recorded after bacteremia was recognized, during SAB evaluation. These data do not show that dental disease caused the bacteremia.

Background: Vancomycin-resistant Enterococcus (VRE) species are common healthcare-associated pathogens that cause difficult-to-treat infections. Whole genome sequencing of patients has revealed a substantial burden of patient-to-patient VRE transmission in hospitals, with patients in intensive care units (ICUs) at particularly high risk of acquisition. However, few studies adequately characterize the pathways of VRE transmission between patients in acute care settings, a necessary step to identify current gaps in infection prevention practices. By harnessing genomic clustering analyses of whole genome sequences of VRE isolates from patients, environmental surfaces, and healthcare providers (HCP) in ICUs, we aim to reconstruct indirect pathways of pathogen movement to identify patterns of VRE spread and opportunities for transmission prevention. Methods and Findings: We collected daily samples (N = 6848) from ICUs in two hospitals over 13 weeks from four main sampling sources: patients, HCP hands, patient rooms, and shared surfaces. Samples were cultured on selective media and sent for whole genome sequencing (WGS). We used genomic thresholds to identify clusters of related VRE isolates and distinguish unrelated isolates. VRE was detected in samples from 20 out of 322 unique occupant-stays (6.22%). VRE isolates were detected from all sampling sources except for shared surfaces. A total of 44 unique VRE isolates were identified, 43 Enterococcus faecium (VREfm) and one Enterococcus faecalis (VREf). Two distinct patterns of VREfm spread were observed: 1) an outbreak setting with observed patient-to-patient transmission and low VRE diversity, and 2) high VRE diversity and pathogen movement between occupant-stays facilitated by persistent HCP and environmental contamination, but no observed transmission events. VRE detection probabilities were not significantly different between occupant-stays in outbreak and non-outbreak settings (OR = 0.63, 95% CI (0.23, 1.83), p = 0.32). However, inclusion of VRE isolated from non-patient samples increased the number of occupant-stays with VRE detection from 6 to 20, a 3.3-fold increase, as compared to patient samples alone. Inclusion of non-patient samples also increased the number of VRE multi-isolate genomic clusters detected by 7-fold. Our findings are limited because sampling was primarily conducted in ICUs. Due to the combination of short ICU stay durations and imperfect test sensitivity, VRE transmission events were probably underdetected. Conclusions: Our findings characterize the complex nature of VRE transmission pathways in ICU settings. Even without an ongoing outbreak, we found substantial evidence of VRE movement between occupant-stays, facilitated by a combination of HCP hands and environmental surfaces. This study highlights the importance of environmental sampling for understanding VRE transmission potential, which is likely to be underestimated using patient sampling alone. We recommend that future studies incorporate follow-up sampling after discharge to better understand the true burden of transmission.

Background: Privacy curtains represent a uniquely difficult infection control surface because they are frequently touched by patients, visitors, and healthcare workers, yet are difficult to disinfect using routine environmental cleaning protocols. We sought to quantify the performance of a novel automated aerosolized hydrogen peroxide system against pathogens on privacy curtains. Methods: This study was performed in an enclosed office space ({approx} 65 m^3), approximating a patient exam room. Four Bacillus atrophaeus (ATCC 9372) biological indicators (BIs) were affixed to a standard polyester privacy curtain. A single one-minute fogging phase was followed by a ten-minute contact/aeration phase (total = 11 min). Four corresponding chemical indicators measured peak airborne aerosolized hydrogen peroxide concentration, and two control BIs were placed outside of the room. Results: All four BIs demonstrated complete inactivation (no growth after seven-day incubation), representing [≥] 4-log10 spore reduction. Peak dosages reached 42 ppm nearest the aerosolized hydrogen peroxide jet outlet, with the lowest value at the near point on the back side of the curtain reaching 27 ppm (exceeding the target dosage of 25 ppm). Control exposures failed to achieve sterilization. Conclusions: A next-generation aerosolized hydrogen peroxide dry-fogging disinfection system achieved full inactivation of resistant spores on both sides of privacy curtains within a short 11-minute automated cycle.

Hard-tissue surgery requires precise spatial control, while conventional computer-assisted navigation presents guidance separately from the operative field. Augmented reality can bring navigation information closer to the surgeon, but how best to translate this information into intuitive intraoperative guidance remains underexplored. We therefore developed an augmented reality navigation system (ANS) that translates real-time deviations from the planned trajectory into co-located holographic action cues. Evaluation comprised typodont and ex vivo porcine experiments, a randomized crossover study in 40 dental trainees, and eight clinical cases. In porcine mandibles, ANS showed lower angular deviation than static and dynamic navigation (1.78 vs 6.42 and 4.02 degrees). Among trainees, accuracy was comparable with dynamic navigation, while workload was lower (29.18 vs 33.15), usability higher (73.13 vs 68.88), and visual-focus ratings improved (3.55 vs 2.28). Clinical cases supported feasibility. These findings support co-located action guidance as an alternative interface paradigm for surgical navigation.

Automated dental implant planning using artificial intelligence (AI) faces challenges in interpretability, safety assurance, and applicability beyond single-tooth cases. This paper presents a unified explainable framework for cone-beam computed tomography (CBCT) that handles both single-tooth and consecutive multiple missing teeth. The method combines nnU-Net-based anatomical segmentation with a transparent geometric optimization pipeline. Implant placement is formulated as a lexicographic multi-objective constrained optimization problem that enforces clinical safety margins while balancing bone engagement, mesiodistal spacing, and buccolingual position. For multi-tooth cases, a global coordination strategy ensures inter-implant parallelism, minimum spacing, and a common insertion path. The framework was validated on 20 single-tooth and 9 consecutive multi-tooth cases. The AI plans achieved a centroid distance of 1.60+/-1.05 mm for single-tooth cases and 1.22+/-0.32 mm for multi-tooth cases, with approximately 7 degrees axial angle difference. All plans achieved 100% compliance with predefined safety thresholds. In a Turing-style test, experts were unable to distinguish AI from human intelligence (HI) plans under blinded conditions. By decoupling perception from reasoning and embedding clinical knowledge as explicit constraints, the framework offers an interpretable and clinically trustworthy solution for computer-aided implant dentistry.

The global dissemination of antimicrobial resistance is increasingly driven by bacterial clones combining antimicrobial resistance with enhanced virulence and environmental adaptability. Escherichia coli sequence type 131 (ST131) has historically been regarded as a major disseminator of the extended-spectrum {beta}-lactamase (ESBL) blaCTX-M-15. However, the emergence of E. coli ST1193 carrying blaCTX-M-15 may represent an ongoing shift in the epidemiology of this resistance determinant. Here, we investigated the prevalence, genomic characteristics, virulence and antimicrobial resistance potential of ST1193 in comparison with ST131. A total of 1,136 E. coli isolates were recovered from touristic and non-touristic environments, hospital-associated samples, and aircraft toilets in Guadeloupe. Isolates were whole-genome sequenced and analysed for antimicrobial resistance and virulence determinants. Additionally, publicly available genomic data comprising 1,215 blaCTX-M-15-positive ST131 and ST1193 isolates were analysed to assess temporal and geographical trends. ST1193 was significantly associated with aircraft-associated samples and exhibited a higher antimicrobial resistance gene burden than ST131, while maintaining a comparable virulence factor content. Analysis of publicly available genomes revealed similar temporal emergence patterns for blaCTX-M-15-positive ST1193 and ST131, with ST1193 showing a more recent distribution and a higher number of deposited isolates in recent years, consistent with a potential ongoing clonal replacement. Comparative genomic analysis identified numerous virulence and adaptation-associated genes shared between both sequence types, while ST1193 additionally carried distinct determinants, including components of the transmissible locus of stress tolerance. Furthermore, quinolone resistance-associated mutations were strongly linked to blaCTX-M-15 carriage, particularly among ST1193 isolates. Together, these findings identify E. coli ST1193 as an emerging high-risk clone with substantial potential for blaCTX-M-15 dissemination. Its association with aircraft-associated samples further highlights the potential role of air travel in long-distance transmission and underscores the need to reconsider current surveillance strategies focused predominantly on ST131.

Background: Healthcare has witnessed administrative staffing roles balloon to twice the number of employed clinicians, resulting in $950 billion per year in administrative costs to deliver healthcare. Administrative workflows, like fax routing, are ripe for automation given the high human labor cost necessary to complete these tasks. Facsimile transmissions remain a key mode of communication in modern healthcare, requiring substantial manpower, and incurring significant, though not well-characterized, costs to health systems. Opportunities may exist for agentic artificial intelligence (AI) to automate this administrative task. Methods: This quality improvement study was performed in 2 phases at Duke University's Division of Cardiology, a single tertiary-care cardiac referral center. The first retrospective phase employed an observational time study design surveying manual fax routing processes at 3 representative cardiology clinics from April 1, 2024, to July 12, 2024. The second phase quantified all inbound faxes received through the division's communication hub from July 1, 2025, to December 31, 2025, and applied direct labor costs observed in the time study to quantify the economic burden of manual fax routing across the hub. Results: The observational time study (Phase 1) demonstrated fax routing processing times ranging from 4.4 to 9.4 minutes depending on fax type, with a mean processing time of 6.0 minutes per fax. On average, the ambulatory clinics received 1,694 faxes per month and spent 169.1 person-hours routing faxes. The divisional communication hub (Phase 2) received 24,420 faxes over the study period, averaging 4,070 inbound faxes and 13,341 pages of information per month. Extrapolating direct labor efforts from the time study, 407 person-hours per month were spent processing inbound faxes. For our institution, this translated to $10,663.40 in total monthly costs, roughly 2.5 full-time equivalents. Conclusion: Manual fax routing represents a substantial, measurable, and previously under-characterized operational and administrative burden. Given the significant opportunity to reduce labor time and costs, our study establishes fax routing as a high-value target for automation. Future work is needed to determine the impact of AI-automated fax routing on the time, labor, accuracy, and economics within clinical settings.

Objective Describe the profile of consumers utilising Emergency Departments (ED) across Victorian hospitals for dental conditions between financial year 2018-19 to 2022-23. Methods Dental conditions were grouped according to the diagnosis categories using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM). Presentations for non-dental conditions were classified as 'Other'. Dependent variables included age, sex, Aboriginal and Torres Strait Islander (Aboriginal) background, language other than English, interpreter required, remoteness, and socio-economic disadvantage according to the Index of Relative Socio-economic Disadvantage (IRSD). The primary outcome evaluated is the relative risk ratio (RRR) of having a hospital ED presentation for a dental condition compared to a non-dental condition and age-specific rates per 100,000 population. Multinomial logistic regression was performed following the backwards elimination method. Results Over the years 2018-19 to 2022-23, 0.55% (n=44,382) of ED presentations across Victorian hospitals by Victorian residents were due to dental conditions. Patients aged 20-44 years have the highest RRR compared to other age groups for dental-related ED presentations. Patients experiencing social inequity in terms of demographic factors such as Aboriginal background, requiring an interpreter, not living in a major city and living in areas of greater disadvantage (IRSD) have statistically significant increased RRR for dental-related ED presentations. Conclusion Patient characteristics were useful for identifying the relative risk of dental-related ED presentations between socio-economic and demographic groups to inform relevant public policy design focus on preventing dental-related ED.

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.

Objective: To determine the prevalence of S. aureus among clinical specimens processed at MeTRH and assess the antimicrobial susceptibility patterns and proportion of MRSA among confirmed isolates. Methods: A retrospective descriptive cross-sectional study was conducted using microbiology laboratory records for clinical specimens submitted for bacteriological culture at MeTRH during the study period. A census approach was used, whereby all eligible laboratory records were included. Clinical specimens were processed using routine microbiological procedures, and suspected S. aureus isolates were identified using conventional methods and the VITEK(R) 2 Compact automated identification system. Antimicrobial susceptibility testing was performed using VITEK(R) 2 Compact, with interpretation based on Clinical and Laboratory Standards Institute criteria. Methicillin resistance was determined phenotypically using cefoxitin susceptibility as the surrogate marker. Data were analysed using frequencies and percentages. Results: A total of 1,082 clinical specimens were analysed, of which 37 yielded S. aureus, giving an overall prevalence of 3.42%. Among the confirmed isolates, 21 (56.8%) were recovered from male patients and 16 (43.2%) from female patients. Pus was the predominant specimen type, accounting for 25 (67.6%) isolates, followed by other specimens at 8 (21.6%) and urine at 4 (10.8%). Antimicrobial susceptibility varied considerably. Resistance was highest to benzylpenicillin and amoxicillin, with 100% resistance among tested isolates. Resistance to amoxicillin-clavulanic acid was 75.0%, while 63.3% of isolates tested were resistant to oxacillin. Susceptibility was relatively high to gentamicin (82.8%), ciprofloxacin (77.3%) and clindamycin (75.0%). All isolates tested against linezolid and vancomycin were susceptible. Among 30 isolates with methicillin susceptibility results, 19 (63.3%) were classified as MRSA and 11 (36.7%) as methicillin-susceptible S. aureus (MSSA). Conclusion: The prevalence of S. aureus among clinical specimens processed at MeTRH was relatively low; however, a substantial proportion of isolates demonstrated antimicrobial resistance, particularly to commonly used {beta}-lactam antibiotics. The high proportion of MRSA is of clinical and public health importance. Routine antimicrobial susceptibility testing, hospital-specific antibiograms, antimicrobial stewardship and strengthened infection prevention and control measures are recommended to support appropriate treatment and limit the spread of resistant S. aureus.

A. Rationale: Medication-related incidents are an important patient-safety concern, particularly in critical-care and inpatient settings where polypharmacy, clinical complexity, frequent therapeutic modifications, and transitions of care increase the risk of medication errors. Systematic analysis of reported incidents can help identify predominant error patterns, potential contributing factors, and priority areas for quality improvement. The present study was undertaken to characterize medication-related incidents and identify areas requiring targeted patient-safety interventions. B. Research Question What are the predominant patterns and distribution of medication-related incidents across critical-care and inpatient units, what patient-care, process, communication, documentation, and system-level factors potentially contribute to their occurrence, and how can these findings be systematically translated into prioritized, evidence-based quality-improvement interventions to strengthen medication safety and reduce preventable medication-related harm? C. Study Design and Methods A retrospective descriptive observational study was conducted to assess the pattern of medication-related errors identified through medication audits conducted in inpatient and critical-care settings. A total of 74 medication audit records were retrospectively reviewed. Each record was evaluated for the presence and type of medication-related error, including transcription, administration, documentation, and dispensing errors. The identified medication errors were categorized based on the nature of the error documented in the audit records. As individual records could contain more than one type of medication-related error, the error categories were considered non-mutually exclusive. Consequently, the total number of error occurrences could exceed the number of records reviewed. Descriptive statistical analysis was performed using frequency distributions and percentages to determine the relative occurrence of each medication-error category. Pareto analysis was subsequently performed to identify the predominant error categories and establish priorities for quality-improvement interventions. The Pareto analysis demonstrated that transcription and administration errors constituted the major contributors to the observed medication-related errors. The identified error patterns were further evaluated using a process-based patient-safety approach to determine potential failure points within the medication-use process. Poka-Yoke (error-proofing) principles were applied to identify opportunities for preventing or detecting errors at the point of occurrence. Based on the findings, potential interventions included standardization of medication-ordering and transcription processes, mandatory verification of medication details, strengthening of independent verification for high-alert medications, and incorporation of technology-enabled safeguards such as electronic medication administration records and barcode-based medication administration. The findings were used to identify priority areas for targeted quality-improvement interventions aimed at reducing medication-related errors and strengthening the safety and reliability of the medication-use process. D. Results: A total of 91 medication-related error occurrences were identified across the 74 audit records reviewed. Transcription errors constituted the predominant error category (46; 50.5%), followed by administration-related errors (41; 45.1%). Documentation-related and dispensing-related errors accounted for 3 (3.3%) and 1 (1.1%) occurrences, respectively. Pareto analysis demonstrated that transcription and administration-related errors collectively accounted for 87 of the 91 identified occurrences (95.6%), indicating a substantial concentration of medication-related errors within these two categories. The cumulative frequency increased from 50.5% for transcription errors to 95.6% following inclusion of administration-related errors, while the remaining categories contributed only 4.4% of the total error occurrences. Accordingly, transcription and administration-related errors were identified as the principal priority areas for subsequent root cause analysis and quality-improvement interventions. Interpretation: The findings demonstrate that transcription errors (50.5%) and administration-related errors (45.1%) were the predominant medication-related errors, collectively accounting for 87 of the 91 identified error occurrences (95.6%). Documentation-related errors constituted 3.3%, while dispensing-related errors accounted for 1.1% of the observed occurrences. Pareto analysis revealed a marked concentration of medication-related errors within the transcription and administration domains, indicating that these two categories represented the principal areas requiring focused quality-improvement interventions. Subsequent Root Cause Analysis (RCA) and Poka-Yoke-based error-proofing approaches were therefore directed toward identifying and mitigating process vulnerabilities within these high-frequency error domains. Keywords: Medication-related incidents, Medication errors, Patient safety, Critical care, Inpatient care, Medication transcription errors, Medication omission, Medication administration, Root Cause Analysis, Pareto analysis, Poka-Yoke, Error-proofing, Quality improvement, Medication safety, PDSA.

Predicted probabilities of antibiotic susceptibility to known or suspected pathogens can be used to support antibiotic selection. Patient demographic, clinical, and prior microbiologic history can be used to generate location-specific prediction models. For three Canadian tertiary care institutions we fit and evaluated models for predicting Gram-negative antibiotic susceptibilities to common antimicrobial agents based on previously established model structures. A web-based application was developed to permit visualization of antibiotic susceptibility predictions and support antibiotic decision-making.

Introduction Anatomical endoscopic enucleation of the prostate (AEEP) techniques, including bipolar enucleation (B-TUEP), holmium laser enucleation (HoLEP), thulium laser enucleation (ThuLEP), and thulium fibre laser enucleation (ThuFLEP), demonstrate comparable clinical outcomes for benign prostatic hyperplasia. As clinical equivalence is increasingly established, cost becomes a key determinant of modality selection. We performed a cost minimisation analysis comparing index procedural costs across AEEP modalities from an institutional perspective. Methods A cost minimisation model was developed from the institutional perspective, incorporating amortised capital costs, maintenance, and consumables. In addition to the base-case scenario of 180 cases per year, we modelled two additional case volume scenarios: low (50 cases/year) and high (500 cases/year) volume. Thu:YAG laser fibres were modelled on two scenarios: disposable single-use, and reusable fibres (up to 10 cases per fibre). Breakeven analysis determined the threshold volume at which each laser modality achieves cost parity with B-TUEP, and one-way sensitivity analysis was performed on key cost parameters. Analysis was limited to index procedural costs calculated in Singapore dollars. Results At the base case of 180 cases per year, B-TUEP had the lowest index procedure cost (SGD 1,018), followed by ThuFLEP (SGD 1,584), ThuLEP (1,599), and HoLEP (SGD 1,655). Breakeven analysis demonstrated that HoLEP, ThuLEP, and ThuFLEP can never achieve cost parity with B-TUEP when laser fibres are single-use, as laser modalities carry higher costs on both capital and per-case dimensions. ThuLEP with reusable fibres (10 uses per fibre) was the only modality to cross below B-TUEP, at a breakeven volume of 198 cases per year. At 500 cases per year with reusable fibres, ThuLEP achieved the lowest cost (SGD 847), representing a 15.4% saving over B-TUEP. Sensitivity analysis identified annual case volume and B-TUEP loop cost as the most influential parameters. Conclusion Index procedural costs in AEEP are strongly influenced by case volume and consumable strategy. While B-TUEP remains cost-efficient at low volume, high-volume practice combined with reusable Thu:YAG fibre technology enables cost parity and potential cost advantage for laser enucleation. These findings highlight the importance of economies of scale and device utilisation in technology adoption.

Background Licensed registered nurses (RNs) who are not employed in nursing are often counted as reserve capacity. A license, however, does not indicate readiness to enter the workforce. We examined whether this population contains distinct workforce activation states and how reported reasons for not working in nursing correspond to those states. Methods We analyzed the 2022 National Sample Survey of Registered Nurses, a nationally representative cross-sectional survey of licensed US nurses. The study population comprised RNs and RN advanced practice registered nurses who were actively licensed but not working for pay in nursing on 31 December 2021. The outcome distinguished five states reported at survey response: no future intention, actively looking, plans future entry or return, undecided, and already entered or returned. Ten prespecified reasons with adequate cell sizes were examined in separate survey-weighted multinomial models. Variance estimation used the main survey weight and 80 successive difference replicate weights. Models adjusted for age, sex, race and ethnicity, nursing education and license type; the family caregiving model also adjusted for marital status and dependents. Results The model-eligible sample included 7,744 respondents, representing 869,236 licensed RNs. An estimated 44.4% had no future intention to work in nursing, 25.6% were undecided, 14.6% had entered or returned, 10.7% planned future entry or return, and 4.7% were actively looking. Retirement was associated with a 27.0 percentage point higher adjusted probability of no future intention (95% CI 21.1 to 32.9). Family caregiving was associated with a 14.6 point higher probability of planned entry or return (95% CI 10.9 to 18.3) and a 13.3-point lower probability of no future intention (95% CI -18.0 to -8.5). Burnout, stressful work, inadequate staffing, scheduling problems and poor management were each concentrated in the undecided state, with adjusted probability differences from 6.1 to 14.6 points. These patterns remained in analyses restricted by retirement, prior nursing work, license type and official imputation status. Conclusions The licensed nonworking RN population is a stratified labor reserve. Family caregiving identifies a planned reserve, workplace barriers identify a conditional reserve, and retirement identifies a largely nonmobilizable group. Workforce planning should measure activation state before treating licensed capacity as available supply.

Background: Cone-beam computed tomography (CBCT) frequently captures the maxillary sinuses incidentally, and reliable automated detection of sinus abnormality is clinically relevant. Unlike most vision-language benchmarks in medical imaging, which pair images with pre-existing, human-authored clinical reports, findings text can also be generated directly by a large language model from the image itself--raising the question of how much diagnostic value such AI-derived text carries, and whether that value depends on independent verification. Multimodal artificial intelligence (AI) benchmarks risk overstating performance if the provenance of each input--image, raw AI-generated text, or radiologist-verified text--is not clearly separated and reported. Methods: We used 300 mid-sagittal CBCT slices from the MMDental dataset. ChatGPT generated findings text and a provisional normal/abnormal label for every slice (majority vote, three independent readings from the image alone); primary classification performance was assessed on this full, unfiltered set (n=300). A radiologist then independently reviewed each case's image together with ChatGPT's description, producing their own diagnosis; three cases were excluded as insufficient, yielding 297 confirmed cases. On this subset, every model was retrained and re-evaluated under identical 10-fold cross-validation on both the provisional ChatGPT-only labels ("pre") and the radiologist-confirmed labels ("post"), isolating the effect of label provenance from image or architecture. Eight vision architectures, seven language classifiers, and five VLMs were evaluated throughout; three generative models performed exploratory note-drafting. Findings: Raw ChatGPT-generated text produced the highest performance of any modality or condition: language models reached near-ceiling AUC (0.992 to 1.000, n=300), exceeding every vision model (AUC 0.799 to 0.880) and every VLM image-only probe (AUC 0.63 to 0.69). On the 297-case pre/post analysis, this advantage depended heavily on label source: language and text-derived VLM performance fell substantially from ChatGPT-only to radiologist-confirmed labels (e.g. BERT-base AUC 0.999 to 0.837), while vision-model performance was stable or modestly improved (e.g. DenseNet-121 0.867 to 0.891). The radiologist reclassified 62 of 297 cases (21%) relative to ChatGPT's provisional read, and a meaningful proportion of raw ChatGPT text was clinically uninterpretable or unsupported by the imaging. Interpretation: As shown here for the first time, raw, image-derived AI-generated text yields the highest apparent classification performance in this benchmark, but this reflects the text's alignment with its own self-generated labels rather than verified diagnostic content, and a substantial share of that text is not clinically explainable. Radiologist-confirmed text and labels give a lower but trustworthy estimate of true performance, on which convolutional neural network (CNN) vision models remain a stable, comparatively inexpensive baseline. Multimodal dental AI should report performance separately by modality and label provenance rather than pooling headline metrics.

Background: Pertussis has resurged in the post-COVID-19 era, yet diagnostic delay remains a barrier to timely treatment and transmission interruption, particularly among adults. Methods: Retrospective observational cohort study of 787 patients with confirmed pertussis from January 2016 through March 2026 across two South Carolina health systems. Electronic health record data from the 60 days preceding confirmation were used to characterize diagnostic timing (same-day vs. delayed), delay duration, and respiratory pathogen testing patterns including pertussis test modality. Results: Adults accounted for 48.4% of cases (median age 16 years [IQR 4-52]; 61.6% female). Same-day diagnosis was highest among children aged 1-4 years (75.4%) and lowest among adults aged 50 and older (approximately 50%). Among the 305 patients (38.8%) with delayed diagnosis, median delay ranged from 7-12 days in children to 22.5-26.5 days in adults aged 50 and older. Delay did not vary significantly by COVID-19 period, race, ethnicity, or insurance type. Testing breadth was greater in inpatient and emergency settings than outpatient. Pertussis testing was predominantly PCR-based in children but serologic in adults, potentially reflecting later clinical presentation relative to illness onset; however, the near-absence of PCR testing in older adults may contribute to missed diagnoses during the early illness window when PCR sensitivity is highest. Conclusions: Older adults experienced markedly delayed pertussis diagnoses compared with children. Earlier consideration of pertussis in adults with prolonged cough, including PCR testing early in illness before serologic conversion, may reduce diagnostic delay and facilitate timelier treatment and contact tracing.

Background: The main cause of urinary tract infections (UTIs) are Gram negative bacteria with Escherichia coli as the leading cause and other important pathogens such as Klebsiella pneumoniae, Pseudomonas aeuriginosa and Enterococcus faecalis. Over the years uropathogens have become resistant to commonly used antibiotics, including penicillin's, cephalosporins and fluoroquinolones. Antimicrobial resistance (AMR) in UTIs is mainly caused by the misuse and overuse of antibiotics, recurrent infections, and healthcare-associated factors such as catheterization. Objectives: The aim of this study was to describe the bacteriological profile and antimicrobial susceptibility patterns of uropathogens isolated from positive urine cultures at Chhatrapati Shivaji Subharti Hospital, Meerut, a tertiary care centre in North India and develop an institutional antibiogram to support empirical prescribing and antibiotic stewardship at this institution. Materials & Methods: The study analysed 50 positive urine culture samples and their antimicrobial susceptibility records from July 2025 to December 2025. The isolates were identified, and antimicrobial susceptibility testing was performed using the disc diffusion method and automated Biomerieux Vitek 2 Compact machine. The collected data was analysed using descriptive statistics and Fisher's exact test. Results: Gram-negative bacteria accounted for 80.0% (40/50) of the culture-positive urine isolates. Escherichia coli was the most frequently isolated uropathogen (n=23, 46.0%), followed by Klebsiella pneumoniae (n=12, 24.0%), Candida spp. (n=6, 12.0%), Enterococcus spp. (n=4,8.0%), Pseudomonas aeruginosa (n=3, 6.0%), and Enterobacter cloacae (n=2, 4.0%). Of the total, 74% (37/50) of isolates came from Inpatient samples. E. coli had a 100% resistance to ampicillin and ceftriaxone, 95.7% to ciprofloxacin and cefepime, and 73.9% to meropenem, with fosfomycin (86.4% sensitive) and colistin (69.6% sensitive) as the only effective antimicrobials. K. pneumoniae had 100% resistance to ceftriaxone, amoxicillin-clavulanate, and piperacillintazobactam; carbapenem resistance ranged from 83.3% to 91.7%, and colistin was the only consistently effective treatment (83.3% sensitive). Of all the 35 tested Enterobacteriaceae isolates, Extended-Spectrum Beta-Lactamases (ESBLs) positivity was 100% with Carbapenem-Resistant Enterobacterales (CRE) positivity at 85.0%. All the bacterial isolates met Multi-drug Resistant (MDR) criteria. 31 of 35 (88.6%) tested Enterobacteriaceae isolates showed ESBL and CRE copositivity. The six strains of Candida demonstrated total sensitivity to all the antifungal drugs used. Conclusion: There is a critical burden of AMR at this hospital with 100% ESBL positivity, 85% CRE, and 100% MDR among all bacterial isolates. This study provides the first baseline institutional antibiogram to guide empirical prescribing and antibiotic stewardship at Chhatrapati Subharti Hospital.

Background: Door handles are frequently touched surfaces that can serve as fomites for transmission of pathogenic and antimicrobial-resistant bacteria. However, contamination of door handles in university settings in low-resource countries remains poorly characterised. This study determined the prevalence, bacteriological profile and antimicrobial susceptibility of bacterial contaminants on door handles at Mbarara University of Science and Technology (MUST) City Campus, Uganda. Methods: A laboratory-based cross-sectional study was conducted using stratified random sampling across hostels, lecture rooms, toilets, laboratories and offices. One hundred door handles were swabbed in the evening using a standardised technique and processed within 24 hours. Samples were cultured on blood and MacConkey agar. Bacterial isolates were identified using colony morphology, Gram staining and standard biochemical tests. Antimicrobial susceptibility was determined using the Kirby-Bauer disc diffusion method according to CLSI guidelines. Results: Of the 100 door handles sampled, 53 (53.0%) yielded bacterial growth, producing 59 isolates from five species, including three cases of polymicrobial contamination. Contamination was highest on toilet door handles (68.2%) and lowest on laboratory handles (20.0%). Gram-positive cocci predominated (89.8%), with Staphylococcus hyicus (47.5%) and Staphylococcus aureus (40.7%) being the most common isolates. Other organisms included Klebsiella pneumoniae (8.5%), Hafnia species (1.7%) and Staphylococcus saprophyticus (1.7%). High antimicrobial resistance was observed against azithromycin (61.0%), tetracycline (44.1%) and penicillin (40.7%). Gentamicin showed the greatest activity, with only 8.5% resistance, followed by chloramphenicol (20.3%). Resistance to cefixime and ceftriaxone among Gram-negative isolates suggested possible extended-spectrum beta-lactamase (ESBL) production, although these drugs were tested on only six isolates. Conclusion: Door handles at MUST City Campus harbour a substantial burden of bacterial contamination, including potentially pathogenic and antimicrobial-resistant organisms. This highlights the potential role of frequently touched surfaces in microorganism transmission within university environments. Recommendations: Routine disinfection of frequently touched surfaces, particularly toilet door handles, should be strengthened alongside improved hand hygiene and prudent antimicrobial use. Further studies should incorporate molecular confirmation of bacterial identification and resistance mechanisms, as well as minimum inhibitory concentration testing.

Objective. A deep learning (DL) model was used to convert smartphone videos of a complete arch implant cast into 3D scans. The aim of current study was to determine if a custom scan body (SB) with geometric features and coating would outperform regular PEEK stock SB in this DL scenario. The DL-derived scan outcomes were compared with those obtained from a conventional splinted open-tray impression and from photogrammetry. Materials and Methods. A maxillary edentulous model with six implants and multi-unit abutment analogs was scanned using four protocols: conventional splinted open-tray impression (CO), photogrammetry (PG; Icam4D), DL using stock SBs (DLS) and DL using custom SBs (DLC). Each protocol was repeated for 10 times. The DL scans were produced from smartphone videos with a high-fidelity, multi-view 3D construction AI model (Neuralangelo). The custom designed SB incorporated geometric features and was fabricated via 3D printing followed by a spray coating. Accuracy (trueness and precision) was assessed using three measurements: Root Mean Square (RMS), linear deviation, and angular deviation. Results. DLC outperformed DLS in both trueness and precision regarding RMS and linear measurements (p<0.001). CO and PG demonstrated the highest RMS and linear trueness, with no significant difference between them (RMS: p=0.93; linear: p=0.663). PG achieved the best precision across RMS, linear and angular measurements. Conclusion. The optimised SB significantly improves the accuracy of DL-based approach for full-arch implant scan comparing to regular PEEK stock scan bodies. While early stage, neural surface reconstruction has potential as a viable option for full-arch implant rehabilitation.

Background: Autonomous ultraviolet-C (UV-C) disinfection systems are increasingly used to supplement manual environmental cleaning, yet evidence-based guidance defining pathogen-specific UV-C dose requirements across representative surfaces remains limited. Aim: To characterize operational UV-C dose requirements for clinically relevant pathogens across diverse high-touch and healthcare surfaces and determine how experimentally derived microbial inactivation can inform operational exposure parameters. Methods: SARS-CoV-2, adenovirus, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Candida auris, and Clostridioides difficile spores were exposed to defined UV-C doses on representative high-touch materials or stainless steel under standardized conditions, including a 10% fetal bovine serum organic soil challenge. Microbial inactivation was quantified by viable recovery. Dose-response analysis and operational modelling were used where supported by the experimental data. Findings: UV-C exposure significantly reduced viable recovery of all pathogens, with substantial differences in the exposure conditions associated with microbial inactivation. SARS-CoV-2 exhibited substantial inactivation at doses as low as 2.6 mJ/cm2, whereas the highest evaluated doses were 1,800 mJ/cm2 for C. difficile spores and 3600 mJ/cm2 for C. auris. For C. auris, multi-dose data estimated that approximately 1,410 mJ/cm2 was associated with a 2-log10 reference reduction, enabling distance-dependent exposure-time predictions. Conclusion: Experimentally quantified UV-C exposures produced substantial microbial inactivation across diverse pathogen classes and surfaces. Integrating delivered dose with microbial reduction provides a quantitative framework for translating laboratory efficacy into operational parameters for autonomous UV-C disinfection.