Neurosurgery

Effective modulation of cortical-subcortical motor circuits is essential for post-stroke recovery, yet progress has been constrained by the absence of non-invasive tools capable of precisely targeting deep brain structures. In this pilot proof of concept study, we explored the feasibility and preliminary neuromodulatory effects of a 12-minute transcranial focused ultrasound (tFUS) protocol targeting the ipsilesional ventral lateral posterior (VLp) thalamus in ischemic stroke patients. Six individuals with upper-limb hemiparesis received individualized, neuronavigation-guided tFUS. Sensorimotor tracking performance improved signiffcantly after a single session. Concurrent EEG revealed reversible beta-power suppression over the ipsilesional motor cortex and enhanced theta-phase synchronization in frontoparietal networks, both of which were associated with behavioral gains. Resting-state fMRI indicated rebalancing of inter-hemispheric motor networks. These preliminary ffndings suggest that thalamic tFUS can modulate both local and networklevel neural activity and is associated with immediate functional improvement, highlighting its potential as a feasible neuromodulation approach for deep motor circuit engagement in post-stroke rehabilitation.

Background: Deep brain stimulation (DBS) is a widely used therapy for neurologic and psychiatric disorders. Conventional DBS delivers highly regular stimulation patterns that suppress pathological activity but can induce stimulation-related side effects, limiting the therapeutic window. Introducing controlled temporal variability through stochastic pulse timing may represent an alternative programming dimension to improve tolerability while preserving clinical benefit. Methods: An adult in their 60's with bilateral Vim DBS underwent evaluation of tonic, pink-noise, and white-noise stimulation patterns delivered through his chronically implanted Boston Scientific Genus system using the Chronos research platform. We assessed tremor and stimulation-induced side effects using accelerometry, spiral drawing tasks, standardized speech recordings, and patient-reported paresthesias. Results: Pink noise stimulation preserved meaningful tremor suppression while improving tolerability compared with conventional tonic 130 Hz stimulation. Under tonic stimulation, dysarthria and paresthesias were prominent at 2.0 mA, narrowing the usable therapeutic window. In contrast, pink noise maintained tremor control across the same amplitude range with reduced side-effect burden. White noise stimulation demonstrated intermediate effects, providing improved tolerability relative to tonic stimulation but less tremor suppression than pink noise. Findings were consistent across accelerometry and functional drawing tasks. Conclusion: This study provides first-in-human evidence that temporally structured stochastic pulse timing can preserve therapeutic benefit while expanding the tolerable stimulation range relative to tonic DBS. These findings suggest that temporal structure represents a clinically meaningful programming dimension that may broaden the DBS therapeutic window using software based updates to existing hardware. Further evaluation in larger cohorts is warranted

Myofascial pain syndrome (MPS) is a major cause of chronic neck pain, with tissue ischemia implicated as a contributing factor. This prospective, single-arm interventional study evaluated the analgesic effect of ultrasound-guided fascia hydrorelease (US-FHR) performed around arteries supplying the neck in patients with chronic neck MPS. Thirteen adults (median age 53.0 years; 38.5% female) underwent US-FHR targeting the perivascular fascia of either the transverse cervical or dorsal scapular artery using 2 mL of normal saline. Pain intensity was assessed by visual analog scale (VAS) at rest and during movement; disability by the 5-item Pain Disability Index, Japanese version (PDI-5-J); and arterial blood flow volume before and after the procedure. The primary outcome, pain VAS during movement, decreased from 49.0 mm (interquartile range [IQR], 44.5-64.0) at baseline to 22.0 mm (IQR, 14.5-31.5) at 15 min and 22.0 mm (IQR, 14.0-34.0) at 1 week (Hodges&-Lehmann median difference, 30.5 mm [95% CI, 24.5 to 36.5] and 28.5 mm [95% CI, 18.5 to 37.0]; both P < 0.001). Pain VAS at rest improved from 21.0 mm (IQR, 13.0-43.5) to 8.0 mm at 15 min and 1 week (median difference, 14.5 mm [95% CI, 9.0 to 24.0; P = 0.001] and 13.5 mm [95% CI, 6.0 to 21.0; P = 0.007]). PDI-5-J decreased from 17.0 (IQR, 10.5-23.0) to 13.0 (IQR, 4.0-17.5) at 1 week (median difference, 5 [95% CI, 2 to 8; P = 0.004]). Blood flow volume increased from 11.2 mL/min (IQR, 4.5-14.4) to 17.2 mL/min (IQR, 6.1-23.7) immediately after US-FHR (median difference, +4.1 mL/min [95% CI, +2.5 to +8.9; P = 0.001]), although transient. One patient experienced transient bleeding that was promptly controlled. In this single-arm feasibility study, US-FHR around the target artery was simple and safe to perform and was associated with reduced neck pain. Because the study lacked a control group, these preliminary findings should be regarded as hypothesis-generating and require confirmation in controlled trials; they may also inform the future evaluation of MPS in other anatomical regions. Trial registration: UMIN Clinical Trials Registry, UMIN000053612.

Abstract Importance: Drug-resistant focal epilepsy affects approximately 30% of children with epilepsy and carries excess mortality, impaired neurodevelopment, and substantial costs. Epilepsy surgery is underutilized despite proven superiority over medical management. MRI-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive alternative to open resection, but comparative evidence to guide procedure selection is limited. Objective: To estimate lifetime outcomes and costs of epilepsy surgery versus medical management for pediatric drug-resistant focal epilepsy, and to provide etiology-informed guidance for choosing between open resection and MRgLITT. Design: Markov decision analytic model with a lifetime horizon, parameterized from published systematic reviews, meta-analyses, and cohort studies. Setting: United States, healthcare payer perspective. Participants: Hypothetical cohort of 10-year-old children with drug-resistant focal epilepsy and a seizure focus <3 cm3. Interventions: Best medical management, open resective surgery, or MRgLITT. Main Outcomes and Measures: Quality-adjusted life years (QALYs), lifetime direct medical costs, incremental cost-effectiveness ratios, and lifetime survival. Seizure outcomes were classified as seizure freedom or disabling seizures. Cost-effectiveness was assessed at $100,000/QALY. Results: Both surgical strategies were associated with a 4.6-year survival advantage, 3.6 additional lifetime QALYs, and lower costs than medical management. MRgLITT yielded 22.64 QALYs at $120,943; open resection yielded 22.62 QALYs at $121,650; medical management yielded 19.00 QALYs at $127,471. The difference between MRgLITT and open resection was 0.015 QALYs, reflecting near-equivalent effectiveness; in probabilistic sensitivity analysis, MRgLITT was optimal in 50.3% of iterations and open resection in 38.3%, with neither showing clear superiority. Etiology-specific analyses favored MRgLITT for focal cortical dysplasia and mesial temporal sclerosis, and open resection for tumor-related and cavernoma-related epilepsy. Conclusions and Relevance: Both open resection and MRgLITT were associated with substantially better lifetime outcomes and lower costs than medical management, supporting early surgical referral. Overall effectiveness between surgical approaches was clinically similar, with neither demonstrating clear superiority; the model suggests epilepsy etiology, rather than expected effectiveness alone, should guide procedure selection between MRgLITT and open resection.

BACKGROUND Accurate differential diagnosis of complex neurological disorders remains challenging due to overlapping clinical features and heterogeneous disease presentations. Although large language models (LLMs) show promise in clinical reasoning, prior studies benchmark performance against clinician consensus rather than biological ground truth. A neuropathologically confirmed benchmark dataset for evaluating diagnostic AI in neurology is currently lacking. METHODS We introduce NeuroBench, a curated benchmark of complex neurological cases with neuropathologically confirmed gold-standard diagnoses, and DIAGNO, a confidence-aware LLM-based system for neurological diagnosis. NeuroBench comprises 203 retrospective case summaries from the Massachusetts General Hospital Brain Cutting Conference with corresponding autopsy-confirmed diagnoses. DIAGNO generated top-3 differential diagnoses, employing retrieval-augmented generation (RAG) for lower-confidence cases. Performance was assessed by three independent blinded adjudicators who evaluated both DIAGNO and neurologists against neuropathological ground truth. RESULTS NeuroBench encompassed 79 unique neuropathological diagnoses, spanning conditions including cerebrovascular disease, brain tumors, neurological infections, and various neurodegenerative and inflammatory disorders. DIAGNO matched or outperformed neurologists in top-3 accuracy (0.67 versus 0.63) and taxonomy-level accuracy (0.74 versus 0.66). In cases of disagreement, DIAGNO was more often correct than neurologists (29 versus 19 cases). Diagnostic concordance between DIAGNO and neurologists was high (90% agreement in top-3 predictions), even when both were incorrect, suggesting strong alignment in diagnostic reasoning. On NeuroBench, DIAGNO also outperformed GPT-4o baseline and DeepSeek R1 across all top-k accuracy metrics. In a real-world evaluation on eight complex cases with differentials from Mass General Brigham, neurologists rated DIAGNO's reasoning favorably (mean 4.03/5) across multiple dimensions of clinical utility and safety. CONCLUSIONS NeuroBench establishes neuropathological confirmation as the appropriate standard for evaluating diagnostic AI in neurology, moving beyond clinician-referenced benchmarking to define the ceiling of diagnostic accuracy. Evaluated against this standard, DIAGNO achieved expert-level diagnostic performance and received favorable clinician ratings in real-world applications, supporting its potential as a clinical decision-support tool in neurology.

Abstract: Background: Lacunar stroke is a common and disabling cerebrovascular disease. Small-vessel vasculopathy is thought to be the most common underlying cause, but this has only been identified on histopathology. 7T MRI allows small vessels to be seen in vivo. This study aimed to investigate rates of small vessel vasculopathy in lacunar stroke using 7T MRI. Methods: Patients with lacunar stroke at an Australian tertiary stroke centre were prospectively screened and recruited to the study. Patients underwent 7T MRI with T1, T2, time-of-flight (TOF), diffusion-weighted imaging (DWI) and susceptibility-weighted imaging (SWI) sequences. Images were interpreted by two blinded neuroradiologists. Results: The likely symptomatic perforator could be identified in 16/19 (84%) of cases. Amongst cases where the symptomatic perforator was observed, 14/16 (88%) of the symptomatic perforator vessels had focal stenosis consistent with steno-occlusive vasculopathy. There were 3/19 (16%) of cases with associated large artery vasculopathy. There were 7/16 (44%) cases where an occluded perforator was seen. The majority of patients had at least one vascular risk factor (15/19, 79%) and there were no cases where non-atherosclerotic vasculopathy was suspected. Conclusions: Lacunar stroke is commonly associated with small vessel vasculopathy, likely due to atherosclerosis, which can be identified in vivo with 7T MRI time-of-flight imaging.

Importance: The transition from medical school to residency forms a national training network, yet its large-scale structure and implications for trainee outcomes remain poorly characterized. Objective: To evaluate the US residency match as a network and assess how institutional position relates to residency placement, educational debt, and specialty choice. Design: Cross-sectional analysis of publicly reported 2025 residency match outcomes. Setting: 107 US MD-granting medical schools and 301 residency institutions with available match data. Participants: 14,616 US MD students matching into residency in 2025 (convenience sample). Exposure: Institutional position within the residency match network, quantified using PageRank network centrality. The relative strength of each school's graduating class was defined as the median centrality of residency destinations across graduates (placement score). Main Outcomes and Measures: Residency placement outcomes, mean medical school debt at graduation, and specialty choice (primary care vs surgical specialties) in relation to institutional position within the residency match network. Network-derived measures were also compared with NIH funding, residency reputation, and student selectivity. Results: Among 14,616 US MD students matched across 107 medical schools and 301 residency institutions (approximately 73.5% of total US MD cohort), network-derived measures of institutional influence closely aligned with benchmarks of institutional standing such as NIH funding, residency reputation, and student selectivity (Spearman's Rho; = 0.72-0.86; all p < .001). Graduate outcomes varied systematically across institutions. Graduates of highly connected medical schools were more likely to match into highly connected residency programs (87.3% for top-quintile vs 41.0% for bottom-quintile schools). Schools with higher placement scores had graduates with lower educational debt, reduced entry into primary care, and increased entry into surgical or competitive specialties. Compared with bottom-decile schools, top-decile schools (stratified by placement score) had 37% lower mean graduate debt, 24% lower primary care entry, and 75% higher surgical specialty entry. Higher educational debt was not associated with entry into higher-compensated specialties. Conclusions and Relevance: The residency match network reflects a hierarchical structure of institutional standing. Graduates of higher- and lower-positioned medical schools experience systematically different residency placement outcomes. These findings provide a population-level, behavior-based perspective on institutional influence and its relationship to training pathways.

Background and Purpose- Prehospital large-vessel occlusion (LVO) scales identify severe stroke syndromes but may not distinguish LVO from intracerebral hemorrhage (ICH). We aimed to prospectively validate the PreICH scale, with the primary diagnostic objective of differentiating ICH from confirmed LVO, and to explore whether additional hemorrhage-oriented variables could refine its performance. Methods- We conducted a prospective observational study of consecutive stroke-code activations evaluated before neuroimaging by a vascular neurologist. PreICH was calculated prospectively. Patients with calculable PreICH and valid final diagnosis were included. The primary diagnostic cohort comprised confirmed LVO and ICH. Secondary cohorts included ischemic stroke versus ICH and the overall stroke-code cohort, including stroke mimics. Multivariable NIHSS-adjusted models identified variables associated with ICH. A modified PreICH score (mPreICH) was derived post hoc and evaluated as exploratory apparent performance. Results- Among 1012 screened activations, 982 patients were analyzed: 597 ischemic strokes, 91 ICH, and 294 stroke mimics. The LVO-versus-ICH cohort included 144 LVO and 91 ICH. NIHSS and RACE were higher in ICH than in ischemic stroke, but did not differ between LVO and ICH (NIHSS, 13 [IQR, 7-20] versus 15 [5-23], P=0.300; RACE, 5 [2-8] versus 6 [2-8], P=0.435). In the LVO-versus-ICH cohort, PreICH showed an AUC of 0.758 (95% CI, 0.696-0.820), whereas RACE did not discriminate LVO from ICH (AUC, 0.530 [95% CI, 0.453-0.607]). The exploratory mPreICH showed apparent AUCs of 0.835 (95% CI, 0.785-0.884) for ischemic stroke versus ICH and 0.798 (95% CI, 0.740-0.856) for LVO versus ICH. Conclusions- In this prospective stroke-code cohort, severity-based scales distinguished ICH from the overall ischemic stroke population but showed limited ability to differentiate LVO from ICH. An exploratory modified PreICH scale incorporating additional hemorrhage-oriented variables improved apparent discriminative performance, including in the LVO-versus-ICH setting. External validation is required before potential implementation in prehospital decision-making.

Purpose: To evaluate the latest open-weight vision-language models (VLMs) on the Japanese Diagnostic Radiology Board Examination (JDRBE), assessing overall accuracy and the effects of image input, reasoning, and language. Materials and Methods: In this retrospective study, 29 open-weight VLMs from 13 developers, released in or after January 2025, were evaluated on 327 image-bearing questions from four years of the JDRBE, a non-public benchmark with low risk of data leakage. Each question was answered by each model with and without the image(s), under three language conditions and with reasoning enabled and disabled. Accuracy was the primary outcome, and within-model differences were tested with paired bootstrap confidence intervals and sign-flip permutation tests with Benjamini-Hochberg correction. Results: In the Japanese condition with image input and reasoning, the leading models reached 73.7% (gemma-4-31B-it), 73.1% (Qwen3.5-397B-A17B), and 72.1% (Kimi-K2.6). On the 2025 subset, these three models (74.1%-75.5%) scored above the mean accuracy of five newly board-certified radiologists who passed the 2025 examination (72%; range, 65%-83%). Accuracy broadly scaled with model size, although compact gemma-4-31B-it matched larger models. Enabling reasoning improved accuracy in nearly all models and the contribution of image input was larger when reasoning was enabled, particularly in higher-performing models. English prompts generally outperformed Japanese prompts. Conclusion: Several open-weight VLMs, without medical adaptation, performed at or above the mean of newly board-certified radiologists on the JDRBE, with both model size and reasoning contributing. The highest Japanese-language accuracy came from a compact model suitable for parameter-efficient fine-tuning and serving on a single graphics processing unit.

Background: Net Water Uptake (NWU) is a non-contrast CT (NCCT) biomarker of early cerebral edema in ischemic stroke, calculated from attenuation differences between ischemic and contralateral non-ischemic brain regions. Manual NWU quantification is labor-intensive and prone to inter-operator variability, limiting clinical uptake and research scalability. We developed and internally validated a fully automated NWU evaluation pipeline. Methods: We analyzed 24-hour follow-up NCCT scans from the AcT (Alteplase compared to Tenecteplase) trial. Infarcts were automatically obtained by segmentation framework based on a synchronous image-label diffusion probability model. The images and extracted infarcts were registered to the standard MNI152 space, allowing us to mirror the infarct onto the contralateral hemisphere symmetrically, regardless of size or tilt angle. Subsequently, the mirrored region was inversely transformed to return to its original space. Voxels outside the range of 20-80 Hounsfield Units (HU) were excluded to remove non-parenchymal tissue. Automated NWU was computed as the percentage difference in mean HU between infarct and mirrored contralateral regions. The agreement with manually determined NWU was evaluated using Pearson correlation, mean absolute error (MAE), and Bland-Altman analysis. Results: Of 1,327 patients in the trial, 298 (22.5%) met predefined imaging-quality criteria for the manual validation analysis, including well-aligned raw NCCT scans in the axial plane and clear parenchymal infarct segmentations. Automated 24-hour NWU showed excellent agreement with manual measurements (r = 0.99). Mean absolute error was 0.18% (95% CI: 0.01-0.46). Bland-Altman analysis demonstrated minimal bias (0.09%) and satisfactory limits of agreement (-4.05% to +4.24%). Ninety-nine percent of cases fell within {+/-}5% of the manually determined value. Conclusions: Our automated mirrored segmentation pipeline enables accurate and reproducible NWU quantification from routine 24-hour NCCT scans, matching expert manual measurements with minimal bias.

Background: Emerging artificial intelligence and machine learning (AI/ML) tools can help generate robust knowledge to support precision rehabilitation approaches for varied patient populations. There is a large amount of research-generated and clinical rehabilitation data available for this purpose; however, a pronounced lack of interoperability prevents large-scale data aggregation. Common data models (CDMs) such as Observational Medical Outcomes Partnership (OMOP) have improved data interoperability across healthcare settings, and more recently, for clinical rehabilitation data, specifically. However, the application of these CDMs to research-generated data has not yet been explored. Therefore, as a foundational step, our study evaluated the breadth and depth of OMOP CDM coverage for data in a multi-site repository of harmonized rehabilitation research data: the Enhancing NeuroImaging Genetics through Meta-Analysis Stroke Recovery (ENIGMA-SR) database. Methods: Two raters independently mapped data elements representing 46 demographics and medical history (DMH) ENIGMA-SR variables and 95 distinct ENIGMA-SR rehabilitation assessments to OMOP standard concepts. Initial rater agreement was assessed for data element inclusion in OMOP and for specific OMOP concepts used (primary metric: Gwet's agreement coefficient [AC]). Mapping differences were reconciled, and final mappings were descriptively analyzed to examine (1) overall OMOP inclusion, (2) inclusion of more granular levels (subscales, items) of complex assessments, and (3) mapped OMOP concept characteristics. Results: Initial rater agreement was good/very good for overall OMOP inclusion of DMH and assessment data elements and for OMOP concepts mapped across almost all assessment data elements (Gwet's AC: 0.79-0.89). Initial OMOP concept agreement was more variable for DMH data elements; however, all mapping differences were successfully reconciled to 100%. Overall, DMH data elements had higher OMOP inclusion than rehabilitation assessments: 84.8% (39/46) vs. 58.9% (56/95). OMOP coverage was particularly limited for complex assessment subscale- and item-level data elements (9.4% [3/32]; 19.2% [14/73]) and did not match the granularity level represented in ENIGMA-SR data for 56.2% (41/73) of complex assessments. DMH and top-level assessment data elements were frequently mapped to multiple OMOP concepts (median: 6, 2; range: 1-23, 1-8), and for > 50% of these data elements the concepts spanned 2-3 different OMOP domains. Conclusion: For ENIGMA-SR, the OMOP CDM has good coverage of DMH data, moderate top-level coverage of rehabilitation assessments, and very limited coverage of assessment subscales and items. This uneven coverage, combined with variability in OMOP concepts and domains mapped to equivalent data points, presents challenges for aggregating clinical and research-generated rehabilitation data into AI/ML-ready datasets. Moreover, software tools currently available to facilitate the mapping process do not effectively accommodate content- and structure-related features inherent to research-generated data. Going forward, the utility of the OMOP CDM to aggregate multi-source rehabilitation data may be improved by expanding the catalogue of OMOP rehabilitation-related concepts, building cross-walks to research-oriented data standards, and adapting emerging computational tools to streamline the mapping process.

Background: Whether a patient's pain improved after emergency department (ED) treatment is read from the record to benchmark EDs, compare drugs, and label research outcomes. It is interpretable only if a post-treatment score is recorded, appropriately timed, and chosen by a fixed rule; its stability across these choices is unknown. Methods: Retrospective measurement study of adult headache visits in a de-identified ED database (MIMIC-IV-ED, 2011-2019). Among treated visits, we quantified reassessment completeness by time window, estimated meaningful relief (a reduction of at least 2 points) under score-selection rules and missing-data assumptions, tested whether reassessment was predictable at treatment, and compared headache with other painful presentations. Results: Among 19,501 visits (15,273 patients), 13,682 (70.2%) were treated. A post-treatment pain score appeared at any time for 77.1% (95% CI, 76.4 to 77.8), but within 2 hours of the analgesic for only 47.9% and within 1 hour for 27.5%. Meaningful relief was 66.9% using the first post-treatment score but 81.0% and 83.4% using the last or lowest score; it was 67.5% under inverse-probability weighting and could be bounded only between 51.8% and 74.4%. Whether a score was recorded was weakly predictable at treatment (area under the curve, 0.566) and unrelated to baseline pain. Completeness was similar across headache strata and comparator painful presentations. In an independent ED (MC-MED, a different EHR), the score-selection effect replicated: relief rose from 71.1% (first) to 80.6% (last) and 83.4% (lowest). Conclusions: Documented pain relief after ED headache treatment was not a stable outcome: it varied with the reassessment window and score-selection rule, was not point-identified for unreassessed patients, and behaved like other painful ED presentations. Programs and research that use documented relief should prespecify the reassessment window, score-selection rule, completeness denominator, and a missing-data range, and favor protocol-timed reassessment.

Background: Remote ischemic perconditioning (RIperC) is a simple, noninvasive neuroprotective strategy based on brief cycles of limb ischemia-reperfusion during cerebral ischemia. REMOTE-CAT suggested a potential functional benefit of prehospital RIperC in acute ischemic stroke. However, its effect on poststroke cognitive outcomes, which may not be fully captured by global disability scales, remains uncertain. Methods: We performed an exploratory cognitive substudy of the multicenter, randomized, double-blind, sham-controlled REMOTE-CAT trial. Patients with suspected acute ischemic stroke within 8 hours, prestroke modified Rankin Scale score <3, and RACE motor score >0 were randomized prehospital to RIperC or sham. RIperC consisted of five 5-minute cuff inflation-deflation cycles during ambulance transfer. At 90 days, patients from one center underwent a standardized neuropsychological battery assessing five cognitive domains. Results: Among 122 patients in the primary analysis, 58 (47.5%) completed neuropsychological assessment: 26 allocated to RIperC and 32 to sham. No statistically significant between-group differences were observed in domain-specific Z scores. Cognitive impairment in at least one domain was numerically less frequent with RIperC than sham (26.9% versus 34.4%). Impairment in more than one domain was also less frequent with RIperC (7.7% versus 21.9%), although the overall distribution of impaired domains did not differ significantly between groups (P=0.244). The largest domain-specific difference was observed for visual memory impairment (3.8% versus 21.9%). Conclusions: In this exploratory substudy, prehospital RIperC did not significantly improve 90-day cognitive outcomes after acute ischemic stroke. Nevertheless, RIperC-treated patients showed numerically favorable trends, particularly in global cognitive burden and visual memory. These hypothesis-generating findings support incorporating standardized cognitive outcomes in future ischemic conditioning trials.

Background/Objectives: This systematic review examines how different mechanisms of Traumatic Brain Injury (TBI) influence post-injury functional independence and aims to clarify whether recovery patterns vary by injury type. A total of 105 studies (n = 59,621) involving adults with TBI were synthesized. These findings can guide clinicians and researchers in predicting outcomes and effectively customizing rehabilitation plans. Methods: A review following PRISMA standards analyzed English-language studies published from 1975 to 2025, assessed functional outcomes using the Functional Independence Measure (FIM) or the Glasgow Outcome Scale-Extended (GOSE), converted them to z-scores, and aggregated them via a random-effects model with inverse-variance weighting to demonstrate their relevance. Results: Recreational TBIs show the highest functional independence (z = +1.77), followed by MVAs (z = +1.56), with falls (z = +0.70) and assault-related TBIs (z = -0.12) showing moderate outcomes, and TBIs with penetrating trauma (z = -1.15) indicating the most adverse results. Conclusions: TBI mechanisms appear to meaningfully influence long-term post-injury functional independence. Highlighting this can inspire clinicians and researchers to trust these insights to improve prognosis and rehabilitation strategies, underscoring their crucial role in advancing patient care.

Introduction: Acute compartment syndrome (ACS) is a limb-threatening complication of extremity trauma that requires timely diagnosis to prevent irreversible muscle and nerve injury. Current diagnostic methods are invasive, intermittent, and operator-dependent. We evaluated the feasibility of a novel, Bluetooth-enabled electrical impedance myography (EIM) device (mAlert, Myolex, Inc., Brookline, MA, USA) for continuous, noninvasive detection of ACS-related tissue changes. Methods: Ten Yorkshire swine underwent anterior tibial compartment monitoring using three ACS models: albumin infusion (ALB, n=3), femoral artery and vein ligation (LIG, n=3), and combined albumin infusion plus ligation (ALB+LIG, n=4). Resistance (R), reactance (X), and phase (P) were measured every minute across 1 to 199 kHz alongside continuous intra-compartmental pressure (ICP) monitoring. Group differences in normalized impedance trends were evaluated using the Kruskal Wallis test with Dunn post hoc correction. As a proof-of-concept human study, nine healthy volunteers wore the device for up to five days to assess electrode durability and signal stability. Tissue ischemia was validated using pimonidazole immunohistochemistry. Results: ALB infusion produced progressive, frequency-dependent decreases in R, X, and P, whereas LIG produced consistent increases in R and X across frequencies. The ALB+LIG model generated mixed responses, reflecting the competing effects of edema and ischemia. Normalized phase slopes differed significantly among groups (H=6.14, p=0.046), with post hoc testing showing significant divergence between the ALB and LIG models (p=0.041). Control limbs remained stable throughout monitoring. Pimonidazole staining confirmed hypoxic injury in the intervention limb. In the human pilot study, three participants completed five days of monitoring, demonstrating sustained signal acquisition, while electrode degradation limited data collection in the remaining participants. Conclusions: This preliminary feasibility study demonstrates that wearable EIM can continuously detect model-specific physiological changes associated with ACS in a large-animal model. These findings support further development and clinical evaluation of wearable EIM as a non-invasive monitoring technology for early ACS detection in trauma patients.

Background: Juvenile nasopharyngeal angiofibroma (JNA) is a rare locally aggressive vascular sinonasal tumor that primarily affects adolescent males. Despite advances in endoscopic surgery and preoperative embolization, JNA can be associated with major operative bleeding risk and clinically meaningful recurrence, while non-surgical treatment options remain limited. Methods: To define the cellular programs underlying JNA vascularity, we performed single-cell RNA sequencing of JNA tumors (n=2), tumor-adjacent mucosa, and control sinonasal tissue. We analyzed cell composition, differential gene expression, pathway enrichment, and cell-cell communication, followed by Drug2cell-based mapping of transcriptional states to candidate therapeutic targets. Results: JNA contained an expanded fibrovascular compartment composed of endothelial cells, fibroblasts, pericytes, vascular smooth muscle cells, and neural crest-like cells. Neural crest-like cells were enriched in JNA but showed relatively limited transcriptional differences from tumor-adjacent tissue. By contrast, endothelial cells demonstrated the strongest disease-associated remodeling, with enrichment of angiogenesis, extracellular matrix organization, hypoxia response, and cell migration pathways. Endothelial cells also showed downregulation of adaptive immune signaling pathways, suggesting reduced immune engagement within the tumor microenvironment. Intercellular communication analyses revealed dense endothelial-stromal signaling across the JNA fibrovascular network. Drug2cell analysis nominated VEGF/VEGFR signaling as a candidate therapeutic vulnerability, with VEGFR-targeting agents predicted to act primarily on vascular and lymphatic endothelial populations. Conclusions: JNA is organized around an angiogenesis-dominant fibrovascular program driven by endothelial-centered signaling. These data support further investigation of VEGF/VEGFR-directed therapy as a potential adjunctive strategy for patients with recurrent, unresectable, or surgically high-risk JNA.

Catastrophic geopolitical conflicts increasingly disrupt the continuity of global medical education, placing immense pressure on clinical training pipelines and forced-migration student groups. While short-term, reactive, remote learning models exist, there are a profound lack of evidence-based implementation templates for medical schools within stable host nations to systematically host and integrate displaced clinical student cohorts mid-stream. This study explores the multi-level barriers and facilitators to hosting displaced medical students across diverse international environments, seeking to establish a rigorous, scalable model of educational sanctuary while advancing implementation science theory in crisis contexts. Employing a qualitative multi-site case study design guided by a critical realist ontology, this study analysed 66 semi-structured interviews with displaced Gazan medical students, hosting lecturers, clinical coordinators, and support staff across the United Kingdom, Malaysia, Pakistan, Turkey, and South Africa, mapping reflexive thematic analysis findings onto the Consolidated Framework for Implementation Research (CFIR). The analysis revealed that while rigid immigration policies, clinical placement caps, and severe cultural distance represent substantial barriers, key facilitators include assessment considerations, flexible placement models, sanctuary institutional cultures, peer networks, and decentralised administrative trust. Strategic administrative approaches, such as classifying displaced students as extended clinical elective visitors rather than full-time matriculants, enabled institutions to accommodate them within existing frameworks. This study demonstrates that public sector higher education institutions can act as vital global sanctuary networks to preserve clinical training pipelines. Crucially, the findings advance implementation science by proposing three novel constructs for the updated CFIR in crisis environments: Agile Implementation Over Perfection within the Implementation Process domain, Protective Leadership Shielding within the Inner Setting domain, and Bidirectional Boundary Subversion at the Inner/Outer Setting interface. This theoretical refinement transforms CFIR from a determinant model for stable, clinical interventions into an active, equity-driven framework for rapid humanitarian response in politically contested environments.

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compressed spectral array (CSA). Methods Using a public dataset of annotated neonatal EEGs, features of the aEEG and CSA were extracted from the left and right centroparietal electrodes. These features were then used to train and test three machine learning classifiers, Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results The trained RF, SVM, and ANN classifiers had areas under the curve (AUC) of 0.80, 0.69, and 0.79 for capturing seizure time periods and an average accuracy of 0.91, 0.90, and 0.92 respectively for capturing seizure and non-seizure time periods. Median accuracy scores were higher among patients without hypoxic-ischemic encephalopathy (HIE; median = 1 for all three classifiers) than HIE patients (median = 0.92, 0.93, 0.93). Conclusion A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.

Background/Objectives: Traumatic Brain Injuries (TBIs) often cause profound functional impairments, yet the influence of TBI mechanisms on stair-climbing functional independence over extended timelines remains poorly understood. This study assesses whether Rasch-transformed FIM Stairs scores varied by TBI mechanism over follow-ups spanning 10 years or more. Methods: Data from the TBI Model Systems database were analyzed. The original 30,768 data entries were reduced to 6,226, corresponding to individuals with at least 10 years of data. Functional Independence Measure Stairs data were transformed to logit units via Rasch analysis before being evaluated with a linear mixed-effects regression, incorporating TBI mechanisms, age, follow-up time, and their interactions, with random effects accounting for the participant ID and pre-injury residence location. Results: TBI mechanisms meaningfully shape very long-term stair-climbing. Gunshot wounds and pedestrian-related accidents are associated with poorer performances, whereas motorcycles, bicycles, unclassified vehicular accidents, winter sports, other sports, and fall-related TBIs demonstrated relatively better function. Age, follow-up time, and their interaction also reached significance. Conclusions: Stair-climbing recovery trajectories over extended time significantly vary by TBI mechanism, with individuals with TBIs from gunshots and pedestrian-related accidents showing the most unfavorable recoveries. These findings support the development of mechanism-specific prognostic guidance and individualized rehabilitation strategies, thereby encouraging tailored approaches to improve outcomes.

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.