Value in Health

Background. Tertiary clinical genomics reports condense layered molecular findings into documents that treating oncologists must read, translate, and act upon; manual summarisation of these reports is time-consuming and variable. Tools that assist summarisation and translation into local languages are emerging, yet the field lacks an agreed methodology for evaluating such tools before any downstream clinical use. The appropriate first endpoint is fidelity of the generated summary to its source report, assessed by qualified human raters under blinded scoring, not downstream variant classification. Methods. QNOMX-VHIR-CPSP-001 Phase 1 is a single-site, non-interventional clinical performance study conducted at Vall d'Hebron Institut de Recerca (VHIR) under ISO 20916:2019 as a Clinical Performance Study Protocol. De-identified tertiary cancer genomics reports from pediatric oncology cases are summarised by the AI-assisted summarisation system under evaluation and, in parallel, by the standard manual workflow. Qualified raters score both summary types against the source genomics report using the Quality Summary Index (QSI), a six-dimension, five-point rubric adapted from the Provider Documentation Summarization Quality Instrument, under a blinded, counterbalanced, two-period crossover with a minimum fourteen-day washout. Two co-primary composite endpoints, content and presentation, are analysed for non-inferiority under a Bayesian hierarchical model, with a frequentist linear mixed model as the convergence check. Inter-rater reliability is reported as Krippendorff's ; a Monte-Carlo power analysis of the fixed clustered design is pre-specified. Discussion. The design isolates summarisation quality from clinical decision-making by scoring both summary types against the same source report under blinding, counterbalancing, and a fourteen-day washout. Conclusion. The QSI rubric, the counterbalanced crossover, and the pre-specified Bayesian primary with frequentist convergence check define a replicable protocol for early-stage evaluation of AI-assisted summarisation in tertiary genomics reporting; observed variance components will inform sample-size determination for Phase 2.

Background High-risk preoperative triage remains fragmented: existing tools often estimate risk without identifying modifiable mechanisms or linking classification to postoperative monitoring, destination planning, and rescue resources. This protocol describes implementation and evaluation of a Reserve-Stress-Rescue (RSR Framework), pathway that operationalizes perioperative high risk as a mismatch among patient physiologic reserve, procedural stress, and system rescue capacity. Approach RSR is a proposed clinician-facing, modular scoring framework for adults undergoing major surgery, especially patients with frailty, multimorbidity, poor functional capacity, anemia or malnutrition, cardiopulmonary disease, or limited postoperative support. Each domain, Reserve, Stress, and Rescue, is scored from 0 to 4 and recorded as both a three-part profile and a total score from 0 to 12. Scores map to Green, Amber, Red, and Crimson triage bands that trigger escalating actions, including targeted optimization, multidisciplinary review, anesthesia and surgical planning, postoperative destination selection, monitoring intensity, and predefined escalation criteria. Validation Plan The initial phase of this study received an exemption determination from the Yale University Institutional Review Board on June 3, 2026, under IRB Protocol ID 2000042729, with exempt categories 2(ii) and 4(iii), including a waiver of HIPAA authorization for access to and use of protected health information as described in the approved protocol. Evaluation will proceed in stages, assessing feasibility, interrater reliability, completeness, acceptability, discrimination, calibration, and clinical utility. Key outcomes include postoperative complications, unplanned escalation of care, intensive care utilization, failure to rescue, mortality, length of stay, triage burden, low-yield testing cascades, and management-changing pathway activation. Conclusion The RSR pathway reframes high-risk status as a modifiable interaction between vulnerability, operative insult, and rescue capacity rather than a fixed patient label. If feasible and valid, RSR may standardize high-risk identification, align perioperative resources with anticipated physiology, improve communication, and support safer, actionable shared decision-making.

Objective: Clinical risk scores intended to guide patient-level decisions can show strong average performance. However, predicted probabilities can be systematically too high or too low in specific subgroups even when overall performance is strong. We audited deployment readiness of a strong end-of-surgery postoperative infection model across clinically relevant subgroups and tested mitigation strategies in miscalibrated subgroups. Materials and Methods: We analyzed out-of-fold predictions for 10,719 surgical procedures at a Swiss tertiary hospital, with 504 postoperative bacterial infection events. Prespecified axes were recorded sex, age stratum, and an EHR-derived physiological-reserve proxy. Within subgroups and pairwise intersections, we evaluated discrimination, calibration, threshold-specific errors, and decision-curve net benefit at the prespecified operating threshold. We compared group-specific isotonic recalibration with Wasserstein-barycenter postprocessing and demonstrated portability in SUPPORT2. Results: Overall AUROC was 0.876. While sex-marginal discrimination was similar in women and men (0.878 vs 0.875), age and reserve stratification revealed deployment-readiness failures. Calibration-in-the-large ranged from -0.86 in frail patients to -2.47 in non-frail patients. At the 0.10 operating threshold, decision-curve net benefit was positive in frail patients but negative in pre-frail and non-frail patients. Isotonic recalibration corrected average physiological-reserve-stratified calibration without worsening Brier scores, whereas Wasserstein postprocessing worsened calibration in most procedure clusters. Discussion: Discrimination-only or sex-marginal evaluation would have missed subgroup failures with clinical-utility implications. Conclusion: Subgroup fairness audits for clinical deployment should jointly evaluate discrimination, calibration, and utility. We implemented the audit as the open-source isitfair framework for identifying deployment-relevant subgroup failures, comparing mitigation strategies, and generating structured reports.

ABSTRACT Introduction Traditional in-person fracture clinics are often overcrowded and inconvenient for patients. Virtual fracture clinics aim to address some of these concerns by improving the efficiency of the orthopaedic service and reducing unnecessary interventions while maintaining safety and quality of care. The RECITAL trial is a non-inferiority randomised controlled trial comparing follow-up care provided at a virtual fracture clinic for people with acute simple fractures to follow-up care provided at an in-person fracture clinic. This study describes the protocol for an economic evaluation of RECITAL where the primary aim is to investigate the cost-effectiveness of a virtual fracture clinic compared with traditional in-person fracture clinic care from a health system perspective. Methods and analysis The RECITAL trial recruited 312 participants with acute simple fractures and randomised them to receive follow-up care provided at a virtual fracture clinic or follow-up care provided at an in-person fracture clinic. We will conduct a within-trial analysis from a health system perspective (primary analysis), as well as a health service, patient and societal perspective. The economic evaluation will estimate the difference in the cost of resource inputs on an intention to treat basis used by participants in the two arms of the trial, allowing comparisons to be made between the in-person and virtual fracture clinics. Data for intervention costs and healthcare utilisation will be collected from trial records, hospital electronic medical records and district performance units. The results of the economic evaluation will be expressed in terms of incremental cost per utility weight gained at 12 weeks and will be plotted on a cost-effectiveness plane. Bootstrapping by resampling will be used to estimate 95% confidence intervals around costs and outcomes, and to calculate the confidence intervals around the incremental cost-effectiveness ratio. A cost-effectiveness acceptability curve (CEAC) will be plotted, which will provide information about the probability that an intervention is cost-effective, given the level of a decision makers willingness to pay for each additional outcome. Ethics and Dissemination The trail was approved by the SLHD Ethics Review Committee (RPAH Zone) (X23-0200 and 2023/ETH01038). The findings will be disseminated through a peer-reviewed journal and conference presentations. Trial registration number The trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ANZCTR; 12623000934640)

Background: Large language models (LLMs) are increasingly deployed in healthcare, where they may adopt different stakeholder perspectives, yet the effect of role-prompting on clinical ethical reasoning remains poorly characterized. Methods: We evaluated three frontier LLMs: Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro across 25 ethically complex medical cases. Each model responded from three stakeholder perspectives (physician, patient, insurer) across three independent runs (675 total responses). Decisions were benchmarked against a six-physician panel. Ethical value prioritization was analyzed from physician- and LLM-provided ranked values. A Patient-Centric Decision Index (PCDI) was developed to quantify LLM decision alignment with patient-preferred out-comes. Results: Among 20 cases with clear physician consensus, LLMs prompted as an insurer reduced alignment with physician majority by 50% for GPT-5.4 (p = 0.004), 45% for Gemini 3.1 Pro (p = 0.008) and 10.5% (NS) for Opus 4.6. The insurer role shifted primary ethical values from beneficence (27%) to financial stewardship (20%) across all LLMs. Conclusions: Stakeholder role-prompting fundamentally alters clinical decisions and ethical value frameworks of frontier LLMs, with the insurer role producing systematic denial of physician-endorsed, patient-preferred treatments. These findings raise the need for standardized LLM patient-centricity benchmarks, and physician oversight when LLMs are deployed in clinical decision-making.

Purpose: This study examined longitudinal trajectories of adaptive functioning in 331 individuals with Angelman syndrome (AS) using the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3) and examined differences by molecular subtype. Methods: A total of 331 individuals (156 females, 47%) with genetically confirmed AS (ages 6 months to 52 years) were assessed between 2018 and 2025, including 207 with a deletion subtype, 63 with uniparental disomy or imprinting defect, and 61 with a UBE3A point mutation. Growth scale values were analyzed using linear mixed-effects models with log2-transformed age. Results: Individuals with deletion subtypes demonstrated significantly lower adaptive functioning across domains compared to those with non-deletion subtypes. Adaptive skills across all Vineland-3 subdomains increased nonlinearly with age, showing faster growth early in life that slowed over time, with largely parallel trajectories across subtypes. Conclusion: Individuals with AS demonstrate slow but steady growth in adaptive functioning that continues into adulthood, with progress varying by molecular subtype. These findings provide updated natural history benchmarks and demonstrate the utility of the Vineland-3 for clinical trials.

Objectives: To assess genetic counselors perspectives on ambient AI adoption and its impact on counselor burnout. Materials and Methods: We utilized a mixed methods approach, surveying burnout using the validated Stanford Professional Fulfilment Index (PFI) before and after ambient AI adoption and exploring adoption perspectives through semi-structured interviews. Results: 64% of participants (16/25) completed the pre-survey, with eleven completing post-surveys (69% response rate for completion of all three surveys). 14/25 participants completed interviews. Ambient AI use was associated with reduction in burnout after 90 days; respondents who reported using ambient AI (vs. non-use) had burnout scores 1.05 points lower, on average (p=0.008). Benefits of adoption included effective use with interpreters, memory aid, summarization of non-templated note sections (e.g. family/social history), and improved patient engagement. Challenges included template customization, variable accuracy, oversimplified medical language, and rapport disruption during consent. Ethical and regulatory considerations included data privacy, bias, awareness of training resources, and concerns about job displacement. Discussion: Ambient AI documentation can reduce documentation burden and burnout among genetic counselors. By evaluating both outcomes and real world implementation considerations, our study provides evidence to guide scalable integration of AI enabled documentation tools in clinical genomic medicine. Conclusion: Ambient AI can help support the sustainability of the clinical genetics workforce as genomic medicine initiatives are scaled across health systems. Addressing genetics-specific documentation needs while prioritizing patient trust, transparency, and provider oversight is essential for responsible ambient AI implementation.

Demand for preventative health care is weak in low-income settings. In a field experiment in a low-income, high-risk setting, we evaluated whether demand for a new bio-medical preventative health product, offered free at public health clinics, responds to digital feedback-based intensive information on health risks and benefits of prevention along with a clinic referral enabling access to the product. In our sample of women aged 18-24 years, we find a large correction in risk beliefs sustained six months after the intervention. Against a background of very low baseline usage, within six months we find a 5.8 percentage point increase in take up of the prevention method, a level of uptake which is very large relative to the control group. Reassuringly, there is no meaningful difference in up-take amongst baseline high- risk and low-risk individuals.

Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measures only partially capture the patient experience. We used frontier large language models (LLMs) on patient and caregiver narratives at scale to describe how burden shifts with disease course. Methods. We analyzed 189 testimonials from the Peggy Lillis Foundation corpus, sorted into four cohorts with recurrence (r) and fulminant (f) severity as axes (rfCDI, fCDI, rCDI, non-rfCDI). Two independent LLMs coded eight thematic domains, four fulminant flags, thirteen emerging semantic fields, the dominant dimension, and narrative arcs. Two clinicians independently coded a subset for inter-rater reliability (PABAK, Gwet's AC1). Results. Treatment trajectory was the dominant theme in recurrent disease, whereas death and near-death dominated non-recurrent fulminant narratives. Psychological burden was near-universal in fulminant disease (98.0% in rfCDI, 97.2% in fCDI). Caregiver and bereavement content concentrated in fCDI (66.7%). Diagnostic failure was frequent across recurrent cohorts (47.6 - 56.1%). Bacteriotherapy tracked recurrence (60.2% rfCDI versus 5.6% fCDI). Financial, mental-health, and caregiver burdens were prominent and are currently unaddressed by guidelines. Human-human reliability was substantial (PABAK 0.79 for semantic fields, 0.76 for domains); arc coding was least reliable. Conclusions. Patient narratives reveal a course-dependent, multidimensional burden in CDI. Concrete gaps exist between what patients prioritize, what guidelines recommend, and what therapy access provides. Frontier-LLM coding, validated against clinicians, offers a reproducible route to translate these priorities into research, care, and policy.

Objectives. To present DrugSet, a validated R Shiny application supporting the construction medicinal products codelists based on the Anatomical Therapeutic Chemical (ATC) system and their mapping to Clinical Practice Research Datalink (CPRD) Aurum prodcodes within a single interactive workflow. Materials and Methods. DrugSet comprises four modules: data preparation, ATC-based hierarchical code selection, string-based CPRD Aurum prodcodes mapping, and codelist export. Validation was conducted against World Health Organization (WHO) ATC reference codelists and manually curated prodcodes mappings across three drug classes: metformin, beta-blocking agents, and topical salicylic acid. Sensitivity, specificity, and Positive Predictive Values (PPV) were calculated for ATC codelist generation. Agreement proportions (overlapping against total identified codes) were calculated for prodcodes mapping. Time needed for codelist construction using DrugSet was recorded and compared to manual approaches. Results. DrugSet ATC codelist generation against WHO manual reference achieved 100% sensitivity, specificity, and PPV across all medicinal products. Prodcodes mapping agreement ranged from 89.2% to 98.3% with discrepancies due to missing data in the prodcodes input vocabulary. DrugSet completed codelist construction in 9 minutes compared to 3 hours and 10 minutes manually, across all medicinal products classes. Discussion. DrugSet provides a unified workflow that runs directly on ATC and source CPRD Aurum vocabulary files. The reduction in codelist construction time and export of the generated codelists supports reproducibility in pharmacoepidemiologic studies where codelist creation can represent a significant proportion of study setup time. Conclusion. DrugSet is an open-source, validated tool that improves accuracy, and efficiency of codelist construction for medicinal products based on ATC codes towards CPRD Aurum prodcodes.

Incidence of type 2 diabetes is increasing at ages when education, work, family, and financial transitions are taking place, yet we lack robust evidence of whether earlier treatment changes life-course outcomes and over which time span this takes place. This paper uses the medical cutoff for diabetes diagnosis (HbA1c of 6.5 percent) as a natural experiment to study the effects of diabetes treatment using electronic health records (EHR) and panel data. This paper has three main findings. First, using EHR data, we find that there is a sharp increase in the probability of both diagnosis of diabetes and prescription when the HbA1c equals 6.5 percent. Second, we find that treating diabetes reduces HbA1c levels, weight, BMI, and blood pressure and increases the amount of care received, proxied by the number of HbA1c tests. Both the diagnosis and a prescription are independently able to produce positive changes in metabolic health, although a prescription is more effective in this regard. Third, we conclude that treating diabetes does not have a significant effect on life-course outcomes for a cohort of young Americans aged 24-32, although it does result in a reduction in HbA1c levels that are seen even eight years after the intervention. Taken together, these findings suggest that receiving a diagnosis and prescription are both effective treatments for diabetes, but they do not translate to significant alterations in the lives of young adults in the medium-term.

Background: The Cilostazol Albumin Treatment in Subarachnoid Hemorrhage (CATS) trial evaluates eight active cilostazol-human albumin regimens plus control in patients with aneurysmal subarachnoid hemorrhage. We summarized the rationale for the primary statistical design, compared alternative Phase II methodologies, and evaluated reduced-arm sensitivity scenarios. Methods: The binary primary endpoint is Common Data Elements-defined delayed cerebral ischemia within 14 days after randomization. The selected design is Bayesian adaptive, with a burn-in phase, response-adaptive randomization among active arms while maintaining fixed control allocation, four interim analyses, early stopping for expected success or futility, and a two-dimensional normal dynamic linear model. Primary operating characteristics were obtained from 1,000 virtual trials per scenario using Fixed and Adaptive Clinical Trial Simulator version 7.0.0. Exploratory simulations evaluated six-, four-, and two-active-arm configurations and simplified alternative designs. Results: Compared with fixed equal allocation, the Bayesian adaptive design preserved an approximately 10% false-success probability under the global null while improving probability of success and efficiency in clinically relevant scenarios. Under the Realistic scenario, probability of success increased from 0.61 to 0.86, expected sample size decreased from 400 to 308, and expected duration decreased from 235 to 187 weeks. Under common thresholds, null probability of success was 0.098 for the full anchor and 0.073 for Reduced-6; Reduced-6 probabilities of success were 0.774 and 0.765 in the Realistic and Realistic2 scenarios. However, Reduced-6 omitted two monotherapy anchors and was less robust in Backwards2. In the comparator simulation, the selected design had probability of success of 0.858 and expected sample size of 308.3 under the Realistic scenario, compared with 0.624 to 0.845 and approximately 352 to 400 for simplified comparators. Conclusions: For identifying the most promising cilostazol-human albumin regimen for Phase III rather than confirming efficacy, the Bayesian response-adaptive design with two-dimensional normal dynamic linear model borrowing is more efficient and better aligned than simplified comparators. The full nine-arm design remains preferable because it preserves the complete therapeutic discovery space and is more robust to misspecified or non-smooth response surfaces.

Background A drug-repurposing-specific phenome-wide association study (PheWAS) demonstrated that patients with a single nucleotide variant that decreases expression of platelet-derived growth factor receptor beta (PDGFR{beta}) have a higher prevalence of fistulas, suggesting that PDGFR{beta} signaling is important for tissue repair. Recombinant human platelet derived growth factor B (rhPDGF) is an FDA-approved protein-based therapeutic that signals through PDGFR{beta} to heal and regenerate cutaneous skin wounds, periodontal tissue, and orthopedic bone with a strong safety profile. We hypothesize that rhPDGF will benefit other conditions identified by PheWAS with a similar physiological mechanism as the existing indications, such as complex perianal fistulas that are ineligible for a fistulotomy. Methods and analysis This prospective, blinded, single-site study aims to enroll 12 participants, randomized at a ratio of 2:1, comparing implantation of rhPDGF-enhanced collagen to routine care procedures, and stratified by fistula etiology, idiopathic versus Crohns disease (CD)-related. The primary outcome of this study will evaluate the technical performance of the rhPDGF-enhanced collagen implant for treatment of complex perianal fistulas as measured by the proportion of participants with successful implantation of the intervention without any intervention-related serious adverse events. The secondary outcomes will assess the preliminary safety and efficacy of the intervention based on all intervention-related adverse events, total fistulas healed, rate of fistula recurrence, and change in patient-reported symptoms. Complex perianal fistulas, idiopathic or CD-related, remain a major clinical challenge in need of new multimodal treatments aimed at tissue repair and regeneration. Pharmaceutical rhPDGF stimulation of PDGFR{beta} signaling promotes healing of skin, bone, and soft tissue. PheWAS revealed fistulas as a novel indication for repurposing rhPDGF. This protocol aims to evaluate the technical performance, preliminary safety and efficacy, and feasibility of rhPDGF-enhanced collagen for healing and remission of complex perianal fistulas. Ethics and dissemination This trial was approved by the Vanderbilt University Medical Center institutional review board (IRB#240585). Results will be submitted for publication in a peer-reviewed journal.

Background and Aims Barrett's esophagus (BE) is the principal precursor of esophageal adenocarcinoma (EAC), whose incidence has risen sharply in Western countries since the 1960s. Effective, dysplasia stratified surveillance strategies are needed to prevent progression. This study evaluated the cost effectiveness of dysplasia stratified surveillance intervals and endoscopic eradication therapy (EET) across the BE spectrum. Methods We developed an incidence-based Markov state transition model of BE progression calibrated to U.S. epidemiologic data from a healthcare sector perspective over a lifetime horizon. Four hypothetical cohorts of 50-year-old individuals with short segment BE (SSBE), nondysplastic BE (NDBE), low grade dysplasia (LGD), or high-grade dysplasia (HGD) were evaluated. Strategies included no surveillance; surveillance at 1-, 2-, 3-, 4-, 5-, or 10-year intervals; standard or AI assisted endoscopy; non endoscopic screening (sponge, breath, miRNA tests); and EET for LGD and HGD. Outcomes included costs, quality adjusted life years (QALYs), incremental cost effectiveness ratios (ICERs), net monetary benefits (NMBs), EAC cases, and EAC-related deaths. Sensitivity analyses used a willingness to pay threshold of US$100,000 per QALY. Results No surveillance was the most cost-effective strategy for SSBE and NDBE. For LGD, upfront EET was more cost effective than all surveillance strategies, with results sensitive to EAC incidence and recurrence. For HGD, EET was cost saving and yielded the greatest QALYs, with findings robust in 99.9% of simulations. EET prevented 12,614 and 44,295 EAC related deaths per 100,000 individuals with LGD and HGD, respectively. Conclusion Dysplasia-stratified management is essential for optimizing surveillance and treatment strategies in BE. Any degree of dysplasia should receive EET followed by targeted post-treatment monitoring, establishing EET as the central therapeutic pathway for dysplastic BE.

Clinical trials often enrol populations that differ from those who ultimately receive the interventions, raising concerns about external validity and health equity. Trial registries could provide an early opportunity to assess representativeness, but it is unclear whether registry data contain sufficient information to enable such assessments. This study evaluated the feasibility of using registry data to assess representativeness in Phase II and III pharmacological randomised controlled trials. A search of ClinicalTrials.gov from December 2024 to January 2025 identified trials with results posted after 1 January 2023 across cardiovascular disease (CVD) excluding stroke, diabetes mellitus, and selected mental health disorders. Of 1,328 records screened, 98 trials met inclusion criteria (51 Phase III, 47 Phase II). Reporting completeness was variable, particularly in Phase II studies. CVD and diabetes trials predominantly included middle-aged to older adults, while mental health trials recruited mainly individuals aged 36 to 50 years. Across CVD and mental health trials, participants were largely male. Reporting of BMI, contraception, and comorbidity criteria was inconsistent, though available data suggested these factors influenced sample composition. Fewer than 10% of trials reported equity-relevant characteristics beyond age and sex, and none addressed intersectionality. Assessing equity using registry data is feasible but constrained by incomplete and inconsistent reporting.

Background Large Language Models (LLMs) are increasingly explored for pharmacovigilance tasks, including information extraction, case documentation, and single-case causality assessment. However, their ability to support causality assessment at the case series level -- a complex, time-intensive task requiring clinical reasoning across multiple reports -- remains unexplored. Objective To investigate how a large-scale general-purpose LLM can support pharmacovigilance professionals in assessing causality in a case series, and to explore how prompt design influences the quality of the model's reasoning. Methods GPT-4o was used to assess causality for five drug - adverse event combinations, using an adaptation of the Bradford Hill viewpoints for case series assessment. The combinations represented varying drugs and vaccines, adverse events, and case series sizes (5-402 reports). One combination served as a negative control. Structured prompts were iteratively developed and refined using one combination, then applied to all combinations. LLM-generated assessments for each viewpoint were qualitatively evaluated by human annotators for accuracy (precision), and the LLM's coverage of key aspects from the original signal text was assessed for one combination (recall). Results Across all five combinations, annotators agreed with 79-92% of the LLM's output sentences. Full disagreement was consistently low (3-7%), with errors typically involving misinterpretation of complex report details rather than outright fabrication. Prompt design substantially influenced output quality; providing Bradford Hill viewpoint descriptions, including case series data, and adding explicit anti-hallucination instructions improved specificity and grounding. For the recall assessment, 15 of 23 key segments from the original signal text were reflected in the LLM output. The overall summary assessments demonstrated balanced reasoning, correctly distinguishing between positive safety signals and the negative control, and provided a coherent synthesis suitable as a starting point for human assessors. Conclusions LLMs have the potential to generate contextually nuanced and largely accurate preliminary causality assessments of case series aligned with the Bradford Hill viewpoints, with a low but non-zero hallucination rate. These findings support LLMs as a tool to augment, not replace, expert judgment in signal assessment. Future work should address larger and more diverse signal sets, improved evaluation frameworks for generative output, and the integration of pre-computed summary statistics to reduce errors.

Laser Interstitial Thermal Therapy (LITT) is a minimally invasive neurosurgical technique in which a stereotactically-implanted fiber delivers thermal energy to ablate intracranial lesions. Existing computer-assisted planning systems optimize trajectories against a one-dimensional line abstraction, then approximate the ablation zone as a fixed-radius cylinder post-hoc to estimate coverage. Trajectories selected as optimal under this model are not guaranteed to remain optimal once the cylindrical extent is applied, which introduces a mismatch between predicted and true ablation coverage. This may also underestimate spillover into surrounding healthy tissue. We present OptiLITT, a treatment planning system that represents the laser probe as a cylindrical ablation volume from the onset of optimization, jointly solving dual-fiber placement, lesion coverage, and healthy-tissue spillover as a single coupled problem. All planning parameters are exposed through a user-configurable graphical user interface supporting intraoperative refinement between planning stages.

Background: Large language models (LLMs) offer promise for systematic review data extraction, but performance in complex multidisciplinary domains and utility for clinical statement generation remain insufficiently described. Objectives: To evaluate Google NotebookLM for AI-assisted data extraction and RAND/UCLA consensus statement generation in a systematic review of IBD, obesity, and cardiometabolic comorbidities. Methods: Studies were organized into domain-specific notebooks; structured prompts generated standardized evidence tables. Two independent reviewers validated outputs against full-text articles using a four-category error classification. Cell-level accuracy and critical accuracy (cells free of major factual errors) were the primary metrics; workflow time was compared against a published conventional extraction benchmark. Concordance between AI-generated and expert-finalized statements was assessed. Results: Across 57 articles, 1,710 data cells were extracted; 151 (8.83%) were flagged, yielding 91.17% cell-level accuracy. Major factual errors occurred in only 4 cells (0.23%), for a critical accuracy of 99.77%. Most errors were minor omissions (59.6%) or incomplete extractions (30.5%); domain error rates ranged from 7.08% to 11.33%. The pipeline required 17.7 versus a projected 165.1 person-hours (89.3% reduction). PICO-structured prompting generated 70 candidate statements; 58 of 112 finalized panel statements (51.8%) were AI-derived, and 85.7% were retained in the finalized set. Conclusion: Google NotebookLM demonstrates feasibility as a primary extraction and synthesis tool in a multidisciplinary systematic review, with extractive incompleteness as the principal limitation and substantial time savings over conventional approaches. Its novel application to RAND/UCLA consensus statement generation extends AI-assisted evidence synthesis to clinical consensus generation workflow.

Background. Evidence of the effectiveness and cost-effectiveness of new vaccines that reduce the burden of respiratory syncytial virus (RSV) in older populations is emerging. The reported cost-effectiveness of these vaccination strategies varies substantially across different settings. This study assessed the cost-effectiveness of older adult-targeted RSV vaccination strategies in the Australian context and compared findings with published evaluations. Methods. We developed an individual-based dynamic transmission model of RSV infection, linked to a clinical pathways and cost-effectiveness model. We modelled different adult vaccination strategies for the general population and the Indigenous population, and present incremental cost-effectiveness ratios (ICERs) as cost per quality-adjusted life year gained, from a healthcare system perspective. Deterministic and probabilistic sensitivity analyses explored drivers of cost-effectiveness and sensitivity of findings to uncertainty in parameter estimates. Results. Vaccinating the general population of older adults in Australia was not found to be cost-effective at a dose price of 100 AUD, but was found to be cost-saving for Indigenous adults, given the higher disease burden in this population. Individual drivers of ICERs in our setting were dose price, hospitalisation incidence and mortality, however conclusions about cost-effectiveness were robust to joint parameter uncertainty. Conclusions. The cost-effectiveness of vaccinating adults against RSV depends on many uncertain and context-specific quantities. Strategies that target high risk populations were found to be cost-effective in Australia due to the larger avertable burden.

Necrotizing enterocolitis (NEC), frequently resulting in sepsis, is among the leading causes of morbidity and mortality of pre-term newborns. However, diagnostic and therapeutic strategies for NEC and sepsis are still limited and controversial. In this context, there are ongoing debates regarding the application of human milk-based fortifiers (HMF) versus bovine milk-based fortifiers (BMF), but robust evidence is lacking. Systematic reviews and meta-analyses are expected to provide the highest level of evidence, but they are time-consuming and resource-intensive and are at risk of potential bias and subjectivity. The rapidly progressing large language model (LLM) artificial intelligence (AI) tools thus emerge as a promising complementary methodology for systematic review and meta-analysis. We conceptualized a cross-LLM AI platform meta-research and evidence synthesis workflow, leveraging 3 representative state-of-the-art platforms, ChatGPT, Claude and Manus AI. We analyzed 3371 PubMed-indexed publications. 3 platforms reported highly concordant findings. We found that prior systematic reviews and meta-analyses generally reported mixed findings comparing HMF versus BMF. Our LLM AI-assisted meta-research and evidence synthesis found non-inferiority of BMF to HMF for NEC and sepsis outcomes. Here, we present an unbiased direct head-to-head comparison between HMF and BMF in the context of NEC and sepsis. Our analyses also represent a proof-of-concept example for LLM AI-assisted meta-research and evidence synthesis, supporting the integration of LLM AI methodologies into evidence-based medicine and digital health.