18 articles · 3 topics
Critical care medicine6 articles
★ Flagship · Score 1.00 · ✓ Verified
Permissive Blood Pressure Targets in Pediatric Critical Care
David Philip Inwald et al. — JAMA · September 28, 2026
Why it’s here: This randomized controlled trial investigates hemodynamic support (permissive MAP targets, vasoactive drugs) in critically ill children, aligning with the researcher's interests.
Study Design
This multicenter, unblinded, pragmatic randomized clinical trial enrolled critically ill children receiving invasive ventilation and vasoactive drugs for hypotension across 23 pediatric intensive care units in 3 countries. Patients were randomized to either a permissive mean arterial pressure (MAP) target greater than the fifth percentile for age or usual care with MAP targets set by the attending physician.
Key Results
A permissive MAP target led to a 37.5% reduction in median norepinephrine-equivalent total dose compared with usual care, with no significant difference in the primary outcome of mortality and duration of invasive ventilation at 30 days (probabilistic index, 0.52). Six of seven secondary outcomes were similar, though the median PICU length of stay was shorter in survivors in the permissive MAP target group (159 hours) versus usual care (166 hours).
Why It Matters
Adopting a permissive MAP target did not result in a significant difference in the primary composite outcome of mortality and duration of invasive ventilation at 30 days compared to usual care. While vasoactive drug exposure was reduced and PICU length of stay was shorter in survivors, these findings suggest no significant superiority for the primary outcome.
Score 0.82 · ✓ Verified
Duration of antibiotic therapy in sepsis and severe infections in critically ill patients
Jean‐François Timsit et al. — American Journal of Respiratory and Critical Care Medicine · September 28, 2026
Why it’s here: This article directly addresses antibiotic duration in sepsis and critically ill patients, including discussion of randomized trials and critical illness factors.
Study Design
This review synthesizes current understanding of antibiotic treatment duration in critically ill patients with sepsis and severe infections. It integrates mechanistic insights from microbiology and PK/PD with evidence from randomized trials and biomarker-guided strategies.
Key Results
Randomized trials consistently demonstrate that short course therapy (<7 days) is noninferior to longer courses for various serious infections, including bacteremia, intra-abdominal infections, and ventilator-associated pneumonia. Biomarker-guided strategies, particularly procalcitonin-based algorithms, may further individualize duration and reduce exposure.
Why It Matters
These findings support markedly reduced antibiotic durations for several common infections after effective source control, balancing efficacy with risks like microbiome disruption and resistance. However, uncertainty remains for specific challenging infections, including S. aureus bacteremia and those caused by multidrug-resistant organisms, highlighting the need for further research.
Score 0.82 · ⓘ Reference
Linna Huang et al. — CHEST Journal · October 1, 2026
Why it’s here: This article is a Phase 2 randomized trial for ARDS, directly matching the researcher's interest in ARDS and randomized trials in intensive care.
Abstract unavailable — listed as a pointer; not summarized.
● Preprint · Score 0.76 · ✓ Verified
Validation of an EEG-based Delirium Monitor in the ICU
Onyemekwu, C. A. et al. — medRxiv Preprint · intensive care and critical care medicine · September 29, 2026
Why it’s here: This article directly addresses delirium monitoring in the ICU using EEG, which is highly relevant to critical care medicine and ICU outcomes.
Study Design
This multicenter, prospective cohort study aimed to validate a point-of-care EEG-based system for delirium detection in adult ICU patients. Participants were monitored with the Ceribell EEG system for 6-8 hours over at least three days, with delirium assessed by expert clinicians and research coordinators using CAM-ICU.
Key Results
Among 225 participants, expert clinicians identified delirium in 24% of assessments. The Ceribell EEG system demonstrated a sensitivity of 81% (95% CI: 76% to 87%) and a specificity of 81% (95% CI: 78% to 84%) in detecting delirium, and its continuous score correlated with CAM-ICU-7 severity scores (median rho 0.58).
Why It Matters
The Ceribell EEG system shows potential for identifying delirium in a mixed adult ICU population with good sensitivity and specificity. Its continuous output's association with delirium severity suggests it can characterize physiological patterns across the delirium spectrum, addressing the variability in clinical delirium assessments.
● Preprint · Score 0.70 · ✓ Verified
Shao, L. et al. — medRxiv Preprint · intensive care and critical care medicine · September 29, 2026
Why it’s here: This study directly addresses the clinical management of insulin in critically ill adults, focusing on hyperglycemia and hypoglycemia, which are key concerns in intensive care.
Study Design
This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV database to analyze transitions from intravenous insulin infusions to subcutaneous glargine in critically ill adults. The study focused on patients without diabetic ketoacidosis or hyperosmolar hyperglycemic state, examining the ratio of the first glargine dose to the preceding intravenous insulin requirement as the primary exposure. Multivariable Cox models were employed to assess the association with rebound hyperglycemia and hypoglycemia.
Key Results
Among 6,220 transitions, a median glargine dose ratio of 0.41 was observed, with cumulative incidences of 44.4% for rebound hyperglycemia and 6.4% for hypoglycemia. Each 0.1 increase in the ratio was linked to a 4% decrease in rebound hyperglycemia (HR 0.96) and an 11% increase in hypoglycemia (HR 1.11). A ratio of 0.60 versus 0.40 corresponded to 2.7 fewer rebound episodes and 1.7 more hypoglycemia episodes per 100 transitions.
Why It Matters
The findings suggest a gradual trade-off between rebound hyperglycemia and hypoglycemia after discontinuing intravenous insulin, with higher glargine doses relative to the prior requirement associated with less rebound after 6 hours but more hypoglycemia. The optimal basal insulin fraction depends on the clinical priority given to each outcome, and a randomized trial is warranted, potentially including the guideline-implied fraction of about 0.30.
● Preprint · ✦ Something Different · Score 0.53 · ✓ Verified
Rostami, M. R. et al. — medRxiv Preprint · genetic and genomic medicine · September 28, 2026
✦ A change of pace: This genetic study on sepsis susceptibility offers a novel, population-specific perspective that diverges significantly from the clinical and interventional focus of the other articles.
Study Design
This study performed a genome-wide association study (GWAS) and an exome-wide association study (EXWAS) on whole-genome sequencing (WGS) data from 210 Arab ICU patients, comprising 68 sepsis cases and 142 controls with other ICU-related disorders. Key methods involved prioritizing deleterious founder variants based on REGENIE Firth regression p-values, CADD scores, and allele frequency enrichment.
Key Results
The analysis identified 40 associated deleterious founder variants in 31 genes, including a novel start-loss variant in UBE2L3 (c.2T>G; p.Met1?) found in 3 out of 68 sepsis cases compared to 0 out of 142 controls (Fisher's exact P = 0.026). In the All of Us cohort, UBE2L3 missense burden was associated with bacterial infection (SKAT-O P = 2.38×10-3; OR=23.10).
Why It Matters
These findings highlight a novel loss-of-function variant in UBE2L3 associated with sepsis susceptibility in an Arab founder population, with UBE2L3 also scoring highly as a drug target for sepsis according to LLM AI models. Mouse UBE2L3 knockout models further support its role, showing increased IL-1Beta mediated inflammation in sepsis, underscoring the potential clinical relevance of this gene.
Healthcare machine learning6 articles
Score 0.82 · ✓ Verified
Han Jang et al. — npj Digital Medicine · October 3, 2026
Why it’s here: This article directly addresses the use of multimodal large language models for diagnostic tasks in clinical care, including validation and reader studies, aligning perfectly with the researcher's interests.
Study Design
This study introduces B-RAD, a novel BI-RADS-aware retrieval-augmented diagnosis framework for breast ultrasound. The framework mimics radiologist workflows by retrieving exemplars, localizing lesions, and determining BI-RADS categories, learning alignment from unpaired data.
Key Results
B-RAD achieved a biopsy triage AUROC of 0.952 on an independent institutional cohort without fine-tuning, outperforming existing vision-language models. In a reader study, B-RAD assistance improved accuracy, increased inter-reader agreement from moderate to substantial, and reduced missed malignancies.
Why It Matters
These findings demonstrate that retrieval-augmented diagnosis can bridge the expertise gap and enhance accessible breast cancer screening, particularly in resource-limited settings. The framework's ability to improve accuracy and agreement suggests a significant advancement in AI-assisted diagnostic tools.
Score 0.78 · ✓ Verified
Dawen Wu et al. — npj Digital Medicine · September 29, 2026
Why it’s here: This article presents a deep learning model (Strabismus-Net) for strabismus diagnosis, including prospective validation and comparison to clinicians, directly aligning with the researcher's interest.
Study Design
This study proposes and validates a two-tier diagnostic system for strabismus using a large dataset of 84,897 gaze photographs from 9433 individuals for cross-validation and 10,458 photographs from 1162 individuals for prospective testing. The system includes a home-based screening component using primary-gaze photographs and a specialized nine-gaze deep learning architecture, Strabismus-Net, for community physicians.
Key Results
The screening model achieved an AUC of 99.53%, while Strabismus-Net demonstrated AUCs of 97.15% for orthotropia, 99.19% for exotropia, 99.11% for esotropia, and 90.20% for vertical strabismus. The model performed comparably to senior specialists and outperformed junior residents in identifying vertical strabismus and confirming orthotropia.
Why It Matters
This digital health solution, Strabismus-Net, offers state-of-the-art performance for strabismus diagnosis, optimizing workflows and streamlining specialist referrals, thereby addressing diagnostic delays in underserved areas. The system enables accessible home-based screening and assists community physicians in making accurate preliminary diagnoses.
Score 0.76 · ✓ Verified
Machine Learning Model Predicts Three Year Mortality in Cardiovascular Kidney Metabolic Syndrome
Qi Gao et al. — npj Digital Medicine · October 2, 2026
Why it’s here: This article develops and validates machine learning models to predict mortality in cardiovascular kidney metabolic syndrome, directly matching the interest in predictive models in healthcare.
Study Design
This study developed and validated machine learning models to predict 3-year all-cause mortality in 219,561 hospitalized patients with Cardiovascular-Kidney-Metabolic (CKM) syndrome stages 2-4 across 29 medical centers. Extreme Gradient Boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO) models were trained using 101 variables in a derivation cohort and evaluated in internal and center-based validation cohorts.
Key Results
An 8-variable XGBoost model demonstrated superior performance, achieving a receiver operating characteristic area under the curve (ROC-AUC) of 0.831 in the derivation cohort, 0.826 in the internal validation cohort, and 0.813 in the center-based validation cohort. Patients stratified into high-risk groups by this model faced substantially higher risks of 3-year all-cause mortality (hazard ratio [HR], 9.62) and cardiovascular mortality (HR, 12.53) compared to the low-risk group.
Why It Matters
This parsimonious 8-variable XGBoost model offers accurate mortality risk stratification for patients with CKM syndrome, potentially supporting personalized management strategies. A web-based risk calculator has been developed to facilitate its clinical application.
Score 0.72 · ✓ Verified
Mikko Saukkoriipi et al. — npj Digital Medicine · September 28, 2026
Why it’s here: Directly addresses the use of large language models for clinical information retrieval from electronic health records, fitting the core interest.
Study Design
This study evaluated 14 locally deployable open-source large language models for structured patient-specific clinical information retrieval. The evaluation used a dataset of 1664 expert-annotated question-answer pairs from Finnish electronic health records of 183 breast cancer patients, conducted under fully offline conditions.
Key Results
The best-performing model, Llama-3.1-70B, achieved 95.3% accuracy and 97.3% consistency, while Qwen3-30B-A3B-2507 showed comparable performance with lower memory use and latency. Medical-domain or Finnish-specialized models did not demonstrate a systematic advantage over generalist models, and 4-bit quantization largely preserved accuracy while reducing memory requirements.
Why It Matters
Locally hosted open-source LLMs show potential for supporting structured clinical information retrieval from EHRs, but clinically significant errors (2.9% of outputs) and sensitivity to question formulation are limiting factors. Source attribution, prospective workflow validation, and human oversight are necessary before clinical deployment.
Score 0.72 · ✓ Verified
Rapid multi-species malaria parasite detection using deep learning
Frank Weate et al. — npj Digital Medicine · September 29, 2026
Why it’s here: This article details an improved deep learning platform (PlasmoCount 2.0) for malaria detection, including on-device inference and improved accuracy, representing real-world deployment in healthcare.
Study Design
This study presents PlasmoCount 2.0, a comprehensively redesigned deep learning platform for malaria parasite detection. The platform utilizes YOLOv8 for analysis and is trained on diverse multi-species datasets, enabling robust analysis across parasite species, imaging magnifications, and sample preparations.
Key Results
PlasmoCount 2.0 reduces processing bottlenecks by 90% compared to its predecessor, enabling analysis of single images in under three seconds while maintaining high classification accuracy. The platform improves classification performance across included species and enhances generalization to previously unseen Plasmodium species, while also distinguishing white blood cells from infected erythrocytes.
Why It Matters
These advances have the potential to transform automated malaria smear analysis into a practical, standardized platform for routine laboratory research. The platform is deployed as an offline smartphone-compatible application, establishing a foundation adaptable for future clinical and field deployment, including in remote areas with low connectivity.
★ Flagship · ✦ Something Different · Score 0.47 · ✓ Verified
More Than 1 in 5 Children Report Using AI for Emotional Support—Is It Good News or Bad?
Kate Schweitzer — JAMA · October 2, 2026
✦ A change of pace: This article explores the use of AI for emotional support in children, a novel and socially relevant application that diverges from typical clinical prediction or diagnostic tasks.
Study Design
This Medical News article discusses the use of artificial intelligence (AI) chatbots by children for emotional support. It highlights the challenges in developing evidence and mental health safeguards to match the rapid advancement of this technology.
Key Results
Children are increasingly turning to AI chatbots as a source of emotional support. The article focuses on the difficulties in quickly establishing evidence-based practices and mental health protections for this emerging trend.
Why It Matters
The widespread adoption of AI for emotional support by children presents a significant challenge for the mental health field. Developing adequate safeguards and evidence-based approaches must keep pace with the rapid evolution of AI technology.
Hospital logistics and operations6 articles
★ Flagship · Score 0.73 · ✓ Verified
Who Directs the Control Tower? Customer vs. Supplier-Managed Inventory in Supply Chains
Shi Chen, Morris A. Cohen, Hau L. Lee — Management Science · September 28, 2026
Why it’s here: This article directly addresses supply chain management and inventory control, key components of hospital logistics.
Study Design
This paper investigates the preference between Customer-Managed Inventory (CMI) and Supplier-Managed Inventory (SMI) in a two-echelon, one-to-many supply chain with a single supplier and multiple customer warehouses. The study characterizes optimal base-stock policies and equilibrium holding-cost-sharing parameters for CMI and SMI, benchmarking them against decentralized and centralized operations.
Key Results
CMI is preferred when the customer has relatively higher shortage penalties and operates in a more asymmetric distribution network, such as when demand is concentrated or lead times vary significantly. Conversely, SMI is favored under opposite conditions, with neither CMI nor SMI achieving the first-best outcome but typically showing small efficiency losses compared to full centralization.
Why It Matters
The findings suggest that most performance gains from CMI and SMI stem from decision-right transfer, supported by appropriate holding-cost sharing, rather than solely from information or cost sharing. While CMI and SMI are generally efficient, their effectiveness is contingent on the customer's network structure and penalty costs, highlighting the importance of aligning governance with specific supply chain characteristics.
★ Flagship · Score 0.67 · ✓ Verified
Diffusion Approximation Error for Queueing Systems with General Primitives
Anton Braverman, Ziv Scully — Operations Research · September 29, 2026
Why it’s here: This article directly applies queueing theory and diffusion approximation to analyze queueing systems, a core operations research technique relevant to healthcare.
Study Design
This study investigates the steady-state diffusion-approximation error for continuous-time queueing systems with generally distributed primitives. The methodology extends Stein's method, specifically the generator comparison approach, to piecewise-deterministic Markov processes (PDMPs) by utilizing the basic adjoint relationship (BAR) instead of the infinitesimal generator.
Key Results
A common picture of the error decomposition into interior and boundary terms emerges across canonical systems. The interior terms can be bounded using low-order moments, with three moments sufficing when using the Wasserstein distance, while boundary terms require deeper, model-specific insights for sharper bounds.
Why It Matters
This work provides a methodological framework for analyzing diffusion approximation errors in PDMPs, offering a transparent interior/boundary error decomposition. While interior terms are manageable, the study highlights the inherent difficulty in obtaining order-optimal bounds for boundary terms, suggesting the prelimit generator approach as a promising avenue for future research.
★ Flagship · Score 0.65 · ✓ Verified
Integrating upstream and downstream planning to increase biomanufacturing profitability
Mirko Beissner, Martin Grunow — Manufacturing & Service Operations Management · September 28, 2026
Why it’s here: This article applies stochastic optimization to biomanufacturing, which shares operational research principles relevant to hospital logistics, though not directly in a healthcare setting.
Study Design
This study develops a stochastic optimization model to integrate upstream fermentation and downstream purification decisions in biomanufacturing. The problem is formulated as a discrete-time, infinite-horizon Markov decision process to maximize long-term profitability. The model utilizes available process information, including product formation and resin capacity decay.
Key Results
The integrated policy, characterized by control-limit structures, increases profits by approximately 4% for large-scale insulin production compared to a separate decision-making approach. Analytical characterization of the optimal policy exploits the sigmoidal structure of product formation. The study identifies economic conditions under which integration creates value.
Why It Matters
These findings demonstrate that coordinated decision-making across upstream and downstream processes is required for optimal biomanufacturing profitability, as focusing solely on upstream productivity can lead to suboptimal outcomes. The value of integration is particularly significant in low-margin environments like biosimilar production, where downstream costs are critical. The optimal policy can be implemented using simple threshold-based rules with standard process measurements.
Score 0.64 · ✓ Verified
Helle Mätzke Rasmussen et al. — BMC Health Services Research · October 1, 2026
Why it’s here: This study examines the implementation of prehospital assessment units, touching on capacity and cross-sector cooperation within acute care.
Study Design
This convergent parallel mixed-methods study examined healthcare professionals' experiences with paramedic-staffed Prehospital Assessment Units (PAUs) in the Region of Southern Denmark. Data were collected through quantitative questionnaires and qualitative focus groups involving professionals from emergency medical services, emergency departments, general practice, and municipal acute care.
Key Results
Quantitative ratings were predominantly positive, with median scores of 5.00 for acceptability, 4.13 for appropriateness, and 4.25 for feasibility. Qualitative analysis generated themes around patient selection, integration in acute care, and building understanding through shared narratives, reinforcing the quantitative findings.
Why It Matters
Healthcare professionals reported predominantly positive experiences with the PAU model, highlighting its cross-sector contribution and the value of shared experiences. However, challenges in patient selection and integration indicate potential negative implementation determinants that may affect consistent implementation.
★ Flagship · Score 0.63 · ✓ Verified
Cost learning and strategic inventory: who should carry inventory in a vertical supply chain?
Ganesh Balasubramanian, Benny Mantin, Sachin Jayaswal — Manufacturing & Service Operations Management · September 28, 2026
Why it’s here: This article analyzes inventory management and strategic decisions within a supply chain, a key area of hospital logistics and operations.
Study Design
This study employs a game-theoretic model to analyze strategic inventory decisions in a decentralized supply chain with cost learning. The model captures the interaction between a downstream retailer strategically carrying inventory and an upstream manufacturer facing declining production costs with cumulative output.
Key Results
The research identifies conditions for various inventory holding patterns: only the manufacturer, only the retailer, both, or neither. Counterintuitively, the manufacturer should abstain from holding inventory, even at zero holding cost, if the retailer's holding cost is sufficiently low.
Why It Matters
These findings guide manufacturers on inventory decisions influenced by cost learning characteristics and the retailer's holding cost, challenging the notion that low manufacturer holding costs always favor manufacturer inventory. The manufacturer should also refrain from using inventory to accelerate learning when cost learning is certain or benefits are continuous.
✦ Something Different · Score 0.52 · ✓ Verified
Esubalew Belay Yihunie et al. — BMC Health Services Research · October 3, 2026
✦ A change of pace: This article examines medical equipment management challenges in Ethiopian teaching hospitals, offering a perspective on operational gaps in health technology in a low-resource setting, which is distinct from the more common supply chain and process optimization topics.
Study Design
This facility-based cross-sectional study investigated medical equipment management challenges in seven Ethiopian university hospitals between September and December 2024. Data were collected using structured tools with double-entry verification, and descriptive statistical analyses were performed.
Key Results
The primary factor associated with equipment failure was delayed spare-part procurement, reported by all seven hospitals (100%). Budget shortages, lack of spare parts, and skill gaps were also significant issues, each affecting 6 out of 7 hospitals (85.71%).
Why It Matters
The findings highlight critical technical and organizational challenges in medical equipment management within Ethiopian teaching hospitals, leading to frequent failures and service disruptions. Addressing spare-part shortages, budget constraints, and skill gaps is crucial for improving equipment reliability and healthcare service delivery.
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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