18 articles · 3 topics
Critical care medicine6 articles
★ Flagship · Score 0.99 · ⓘ Reference
Martha A Q Curley et al. — The Lancet · October 1, 2026
Why it’s here: This randomized controlled trial investigates prone positioning and high-frequency oscillatory ventilation in pediatric ARDS, directly matching the interest in ARDS and mechanical ventilation.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.94 · ⓘ Reference
Nuanprae Kitisin et al. — Intensive Care Medicine · November 2, 2026
Why it’s here: Directly addresses therapy for metabolic acidosis in critically ill patients via RCT meta-analysis, highly relevant.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.82 · ✓ Verified
Yoshie Haruna et al. — BMC Health Services Research · October 28, 2026
Why it’s here: This systematic review focuses on factors related to work engagement among healthcare professionals in intensive care units, directly relevant to ICU outcomes and the critical care setting.
Study Design
This systematic review, registered with PROSPERO (CRD 42024538670) and following PRISMA 2020 guidelines, explored factors influencing work engagement among healthcare professionals in intensive care units (ICUs). A systematic search of four databases in April 2024 identified 18 studies meeting inclusion criteria, with 15 employing a cross-sectional design.
Key Results
The review identified factors influencing work engagement categorized into four domains: individual, work-related, outcomes, and interrelated factors. Nurses were the primary focus, investigated in 15 of the 18 studies, with two studies on physicians and one on a mixed group.
Why It Matters
Understanding these factors is essential for optimizing the performance of ICU healthcare professionals, who face significant physical and psychological stress. Strengthening engagement requires a comprehensive grasp of these influences and careful consideration of the work environment.
Score 0.70 · ✓ Verified
Long-term outcomes of sepsis survivors in Indian sepsis registry: the LOSS study
Subhash Kumar Todi et al. — Critical Care · October 27, 2026
Why it’s here: This article directly addresses long-term outcomes of sepsis survivors in the ICU, aligning perfectly with the researcher's interest in sepsis and ICU outcomes.
Study Design
The LOSS study was a multicenter observational cohort follow-up study involving adult patients from the SEPSIS INDIA registry who survived hospital discharge. Baseline data including demographics, clinical information, and comorbidities (assessed by Charlson Comorbidity Index – CCI) were collected, and long-term outcomes were gathered via telephone interviews for up to 39 months.
Key Results
During the 39-month follow-up, 41.9% (251 out of 608) of patients died, and 33.2% (202 patients) were readmitted to the hospital. Higher CCI independently predicted long-term mortality (OR 1.403; p < 0.0001) and increased readmission risk, while 19.1% and 16.9% of survivors experienced deterioration in physical and mental quality of life, respectively.
Why It Matters
Sepsis survivors in Indian ICUs face significant long-term mortality, hospital readmissions, and impaired quality of life, with comorbidity burden strongly influencing these outcomes. The findings suggest that chronic comorbidity burden may be more critical than acute illness severity in determining long-term prognosis after sepsis.
Score 0.70 · ✓ Verified
Saieesh A. Rao et al. — Critical Care · October 29, 2026
Why it’s here: Examines diagnostic disagreement in septic shock and its association with mortality and treatment delays, highly relevant.
Study Design
This multicenter, retrospective cohort study analyzed 2,233 septic shock patients across eight acute care hospitals between 2018 and 2023, using Sepsis-3 organ-dysfunction criteria. Shock etiologies documented within the first 48 ICU hours were compared using Cosine Similarity Scores to define inter-clinician disagreement.
Key Results
Inter-clinician disagreement on shock etiology was identified in 9.5% of patients (213/2,233) and was associated with a 1.99-fold increased adjusted odds of ICU mortality among patients with positive blood cultures (95% CI 0.98–4.01). This association translated to a delta-risk of +16.9% mortality points (95% CI +1.1 to +32.7) in the blood culture-positive subgroup.
Why It Matters
Early inter-clinician disagreement on shock etiology serves as a prognostic marker for increased ICU mortality specifically in Sepsis-3 septic shock patients with positive blood cultures. Importantly, this association was not mediated by delays in the administration of antibiotics, fluid boluses, or vasopressors in the studied subgroup.
✦ Something Different · Score 0.48 · ⓘ Reference
Florian Blanchard et al. — Critical Care · October 30, 2026
✦ A change of pace: This article explores the novel use of gamma-hydroxybutyrate for sleep in non-sedated ICU patients, offering a unique perspective on patient comfort and recovery beyond typical physiological interventions.
Abstract unavailable — listed as a pointer; not summarized.
Healthcare machine learning6 articles
★ Flagship · Score 0.87 · ✓ Verified
A clinical multimodal vision language foundation model with fine-grain explainability
Yang Bai et al. — Nature Medicine · October 29, 2026
Why it’s here: This article introduces a multimodal vision-language foundation model for medicine with explainability, directly matching the interest in foundation models in medicine and clinical applications.
Study Design
This study introduces the Explainable Vision Language Foundation Model for Medicine (EVLF-FM), a multimodal foundation model designed to address limitations in current medical AI models. The development and testing involved over 1.3 million samples from 18 global datasets across 10 imaging modalities and six clinical specialties, with external validation on 13,443 independent test samples from 17 additional datasets.
Key Results
EVLF-FM achieved an average accuracy of 0.895 in internal validation for disease diagnostics, outperforming leading generalist and specialist models, and demonstrated reasonable performance in medical visual grounding with an average accuracy at 0.5 intersection over union (Acc@0.5) of 0.842 across nine modalities. External testing confirmed consistent zero-shot and few-shot performance with competitive F1 scores, and human evaluation of its reasoning outputs using the S.C.O.R.E. framework yielded a high overall mean score of 4.25/5.
Why It Matters
EVLF-FM offers broad diagnostic capabilities with fine-grained explainability, potentially improving clinical adoption of medical foundation models by providing transparent reasoning. However, current limitations include the need for additional validation across diverse healthcare settings, and prospective clinical studies are required to evaluate its safety, effectiveness, and clinical utility before routine deployment.
Score 0.78 · ✓ Verified
Erhan Pisirir et al. — Critical Care · October 26, 2026
Why it’s here: Develops and validates a Bayesian network model for predicting blood transfusion requirements in trauma patients, directly applicable to clinical care.
Study Design
This study developed and validated a Bayesian network model to predict blood transfusion requirements in trauma patients. The model was informed by domain knowledge and clinical data, utilizing injury and physiological variables available early after injury. Data from a prospective civilian cohort and military trauma registries (n=2361) were used for development, with evaluation through internal cross-validation and external validation in an independent dataset (n=13,347).
Key Results
The model achieved AUROC values ranging from 0.82 to 0.94 for binary transfusion outcomes across different timeframes. For ordinal volume predictions, Ranked Probability Scores were 0.07 for 0-12 hour predictions and 0.04 for 12-24 hour predictions. In scenario-based evaluations, expert assessments agreed with model predictions in 213 out of 225 (95%) instances.
Why It Matters
Existing prediction models often use fixed thresholds and do not estimate transfusion volume or timing, limiting their utility for real-time decision-making and resource planning. This decision-support approach, which estimates transfusion requirements by volume and timing using early clinical data, may offer more clinically useful support compared to binary threshold models. The study suggests that such a model could provide more practical decision support in trauma care.
Score 0.78 · ✓ Verified
An interpretable graph reasoning framework for enhanced diagnosis in fundus fluorescein angiography
Xiaocong Liu et al. — npj Digital Medicine · October 27, 2026
Why it’s here: Presents an interpretable graph reasoning AI framework for diagnosis in fundus angiography, including prospective validation and comparison to clinicians.
Study Design
This study introduces FFA-RGNet, an interpretable graph reasoning framework designed for diagnosing retinal vascular diseases from sequential fundus fluorescein angiography (FFA) images. The framework explicitly models temporal patterns of fine-grained lesions across imaging phases and anatomical locations. FFA-RGNet was trained and validated on a large, multi-center dataset comprising 25,312 FFA images from 2651 patients.
Key Results
FFA-RGNet achieved an F1-score of 95.38% (95% CI: 92.24%–97.88%) on the internal test set, outperforming nine baseline models and demonstrating consistent performance across multi-center and prospective validation datasets. Its diagnostic performance surpassed that of ophthalmologists, residents, and ophthalmology students, and it improved their diagnostic F1-score by 7.48% to 14.11% when used as an assistive tool.
Why It Matters
The framework enables accurate diagnosis with interpretable reasoning, highlighting its strong potential as a reliable AI system for FFA interpretation and clinical decision support. Comprehensive inference analysis revealed its superior ability to recognize fine-grained semantic entities and learn stable, disease-specific graph reasoning patterns aligned with clinical knowledge.
Score 0.76 · ✓ Verified
Mirage Modi et al. — npj Digital Medicine · October 31, 2026
Why it’s here: Investigates clinical reasoning variability and stability in medical large language models, directly addressing foundation models in medicine and AI safety.
Study Design
This study investigated clinical reasoning stability in three medical large language models (LLMs): GPT-5, MedGemma-27B-Text-IT, and OpenBioLLM-Llama3-70B. The researchers used 355 systematic perturbations of physician-validated oncology cases and trained sparse autoencoders on 1 billion tokens from 50,000 MIMIC-IV clinical notes to analyze internal representations.
Key Results
The models demonstrated significant reasoning instability, with staging accuracy shifting by over 50% based solely on prompt format and generating definitive staging in clinically insufficient scenarios. Sparse autoencoder analysis revealed hierarchical encoding in MedGemma, contrasting with OpenBioLLM's uniform information distribution.
Why It Matters
These findings suggest that internal encoding structures differentially affect retrieval interventions, meaning interventions effective for one architecture may harm another. The study recommends architecture-specific safety validation, as benchmark equivalence does not imply functional equivalence, with implications for AI safety beyond healthcare.
Score 0.72 · ✓ Verified
Gernot Pucher et al. — npj Digital Medicine · October 31, 2026
Why it’s here: This article uses machine learning to predict a clinical outcome (persistent fever) in cancer patients, directly aligning with the researcher's interest in predictive models in clinical care.
Study Design
This study developed and externally validated machine learning models to predict persistent fever in hospitalized cancer patients receiving intravenous antibiotics. The models utilized electronic health records from two academic hospitals, incorporating demographics, comorbidities, laboratory results, vital signs, and temperature patterns for adult cancer patients with fever within 24–48 hours of treatment initiation.
Key Results
The best-performing machine learning model achieved an AUROC of 0.82 internally and 0.78 externally, with temperature-related variables identified as the dominant predictors. Decision curve analysis indicated a positive net benefit across a wide range of thresholds.
Why It Matters
These findings suggest that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data, potentially reducing unnecessary antibiotic escalations and imaging. However, the study emphasizes that clinical use should be adjunctive, not for standalone decision-making, and prospective studies are needed to evaluate safety and real-world impact.
● Preprint · ✦ Something Different · Score 0.50 · ✓ Verified
Horvath, K. et al. — medRxiv Preprint · psychiatry and clinical psychology · October 26, 2026
✦ A change of pace: This article explores simulating psychotherapeutic sessions with generative AI, offering a novel perspective on AI's role in mental health and research beyond direct clinical prediction or diagnosis.
Study Design
This preregistered proof-of-concept study employed a controlled simulation design to investigate in silico psychotherapy research. The study generated 512 automated motivational interviewing (MI) sessions using Llama-3.3-70B-In-struct models for therapists and patients across eight vignettes and eight personas, manipulating common therapeutic factors (CTF) like empathy and alliance building.
Key Results
High versus low CTF manipulation resulted in theoretically expected improvements across all MI treatment integrity (MITI) global ratings, with effect sizes ranging from {beta}=0.54 to 0.95 (all p<0.001). Manipulation checks confirmed fidelity, with WAI-S-O total scores showing a {beta}=3.14 difference (p<0.001) and TES showing a {beta}=14.48 difference (p<0.001). Simulated patient confidence also increased more under high CTF ({beta}=0.17, p<0.001).
Why It Matters
These findings demonstrate the technical feasibility and experimental fidelity of LLM-based psychotherapy simulations, providing a proof of concept for in silico psychotherapy research as a complementary pre-clinical paradigm. This approach showed theoretically concordant effects beyond directly manipulated constructs and participants, supporting accelerated intervention development. Further evaluation is needed to assess applicability across different patient populations, therapeutic modalities, and model architectures.
Hospital logistics and operations6 articles
★ Flagship · Score 0.83 · ✓ Verified
Location-Allocation of Emergency Service Systems in Light Traffic
Xin Wang et al. — Operations Research · October 27, 2026
Why it’s here: This paper directly addresses the location-allocation of emergency service systems using queueing models and operations research, highly relevant to emergency department operations and resource deployment.
Study Design
This study develops tractable queueing-based location-allocation optimization models for emergency service systems. The key innovation is the use of light-traffic asymptotics to explicitly capture decision-dependent effects on response times and uncertainty. These methods turn limiting queueing behavior as call arrival rates approach zero into explicit approximations for performance losses.
Key Results
The developed models enable mixed-integer linear programming formulations with theoretical guarantees on solution quality in the light-traffic regime. Simulations using Singapore lift-trap rescue data demonstrated that the optimized plans reduced projected annual late rescues by about 70% relative to the existing arrangement.
Why It Matters
This research provides a novel approach to connect queueing performance to planning decisions in emergency service systems, offering theoretical guarantees on solution quality. The findings highlight the potential for significant improvements in emergency response efficiency, as evidenced by the substantial reduction in late rescues observed in simulations.
★ Flagship · Score 0.73 · ✓ Verified
Joint Assortment Planning and Inventory Control under General Choice Models
Qiaochu Fu et al. — Operations Research · November 2, 2026
Why it’s here: Addresses joint assortment planning and inventory control under choice models, directly relevant to supply chain and inventory management in operations research.
Study Design
This study proposes a constant-factor approximation framework for joint assortment planning and inventory management under a discrete choice model with dynamic inventory availability. The approach involves determining an initial inventory vector subject to a capacity constraint, using a fluid relaxation to identify candidate assortments. The algorithm then constructs static-allocation-based lower bounds to select the optimal inventory decision.
Key Results
The framework achieves a (κ/4−ε)-approximation guarantee for a broad class of substitutable choice models where the cardinality-constrained assortment optimization problem admits a κ-approximation. This provides the first constant-factor approximation ratios for models like the Markov chain choice model and mixture of MNL models, and matches the state-of-the-art 0.25 ratio for the MNL choice model.
Why It Matters
This work offers a novel algorithmic framework with constant-factor approximation guarantees for a complex assortment and inventory problem. The guarantees can be strengthened under looser capacity constraints or deterministic/Poisson demand distributions, indicating potential for improved performance in specific scenarios.
Score 0.72 · ✓ Verified
Robust mask-based appointment scheduling in primary care practices
Sabrina Schmitz, Martin Comis, Christina Büsing — Health Care Management Science · October 31, 2026
Why it’s here: This article directly addresses appointment scheduling in primary care practices, including walk-ins and no-shows, which is a core hospital operations and logistics problem.
Study Design
This study addresses interday appointment scheduling for primary care physicians (PCPs) by focusing on mask-based systems. It considers both scheduled appointments and unpredictable walk-ins, as well as no-shows, to balance the PCP's workload. The research extends the mask design problem to robust versions to account for demand uncertainties.
Key Results
The paper provides combinatorial interpretations using network flow and design models for mask design, robust mask design, and robust multimask design problems. A solution approach combining binary search with compact formulations of minimum cost flow problems is developed. An extensive case study using agent-based simulation evaluates these mask-based systems against five other systems from the literature.
Why It Matters
The research evaluates mask-based appointment scheduling systems based on key performance indicators like PCP overtime, idle time, and unsuccessful treatment requests or rejected walk-ins. This work offers a robust approach to appointment scheduling in primary care, aiming to improve service quality by managing various demand uncertainties. The findings are evaluated through extensive simulations, comparing the proposed systems to existing methods.
Score 0.70 · ✓ Verified
EXPRESS: Tactical Workforce Planning and Deployment with Timing Coordination Requirements
Gang Li, Anantaram Balakrishnan — Production and Operations Management · October 27, 2026
Why it’s here: Addresses tactical workforce planning and deployment with timing coordination, a direct application of operations research to logistics and scheduling.
Study Design
This study addresses the tactical workforce planning and deployment (TWPD) problem for organizations with geographically dispersed jobs and timing dependencies, motivated by rail infrastructure maintenance. The authors propose a large-scale optimization model with strong timing coordination constraints, integrating resource sizing, assignment, and repositioning decisions.
Key Results
The proposed Composite method, which incorporates valid inequalities and an optimization-based algorithm, dramatically outperforms a standard solver without enhancements on real and randomly generated data. When compared to a manual tactical plan, the method generates a near-optimal solution saving over 15% in annual total cost.
Why It Matters
This research demonstrates the practical value and viability of an optimization model and method for complex tactical workforce planning problems with timing coordination. The findings offer managerial insights and show significant cost savings compared to existing planning approaches.
Score 0.68 · ✓ Verified
Gülin Tuzcuoğlu et al. — Health Care Management Science · October 30, 2026
Why it’s here: Directly investigates the impact of surgical team familiarity on procedure time, a key aspect of OR scheduling and efficiency.
Study Design
This study employs a causal graphical model (DAG) and structural causal modeling with regression analysis to estimate the hospital-wide causal impact of surgical team familiarity on procedure duration. Data from 33,374 procedures performed between January 2017 and November 2023 in a US academic medical center were analyzed, with team familiarity measured by interaction frequency.
Key Results
The analysis revealed a negative causal relationship between surgeon-scrub familiarity and surgery duration, where a one standard deviation increase in familiarity reduced surgery time by 1.9 to 3.7 minutes. Similar, but smaller, effects were found for surgeon-circulator familiarity, while scrub-circulator familiarity showed no significant impact.
Why It Matters
This study is the first to estimate the causal effect of surgical team familiarity across an entire hospital's surgical portfolio, moving beyond prior correlational analyses. The findings suggest that improving team familiarity, especially between surgeons and scrub nurses, can significantly enhance surgical team efficiency, particularly in high-volume hospitals.
✦ Something Different · Score 0.54 · ✓ Verified
Juliet Gamuchirai Nyamasve et al. — BMC Health Services Research · October 28, 2026
✦ A change of pace: This article offers a unique perspective on operational efficiency in blood component production, a niche area within hospital logistics that differs from typical patient flow or scheduling studies.
Study Design
This study employed a detailed process mapping exercise to analyze the blood component production cycle at a national blood service in sub-Saharan Africa. The methodology involved enumerating client pathways and recording active process times across four stages: donor recruitment, blood collection, blood processing and testing, and storage and dispatch. Data capture was supported by the e-Delphyn blood donor management system.
Key Results
The study revealed significant operational complexity, with 31 distinct client pathways identified at the donor recruitment stage alone, where active organization time varied from 6 to 148 minutes. Blood collection had an optimal completion time of approximately 31 minutes, while blood processing and testing required a minimum of 113 minutes under minimum batch conditions. Component production times ranged from 15 minutes for packed red blood cells to a minimum of 555 minutes for cryoprecipitate.
Why It Matters
Process mapping, integrated with time-driven data collection, demonstrated how variability in each blood processing stage impacts overall system efficiency. These granular, evidence-based insights offer a foundation for informing efficiency analysis and resource allocation in blood services within Sub-Saharan Africa and other LMICs. The study did not involve clinical trials.
Curated from OpenAlex, Crossref & medRxiv. Summaries are machine-generated and grounded in each article’s abstract or open-access text; ⚠ flags a summary that needs a second look, and ✦ marks a deliberate change-of-pace pick.
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