17 articles · 3 topics
Critical care medicine6 articles
Score 0.88 · ⓘ Reference
Eduardo Kattan et al. — Intensive Care Medicine · July 15, 2026
Why it’s here: Directly addresses refractory septic shock, vasopressors, and a trial in intensive care, highly relevant.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.78 · ✓ Verified
Georg Gelbenegger et al. — Critical Care Medicine · July 15, 2026
Why it’s here: Directly addresses antiarrhythmic drug use in out-of-hospital cardiac arrest, a critical care scenario, and uses a target trial emulation framework.
Study Design
This target trial emulation analyzed data from the Resuscitation Outcomes Consortium Cardiac Epidemiologic Registry 3 (2011-2015) to compare lidocaine and amiodarone in adults with nontraumatic, shockable out-of-hospital cardiac arrest who received at least three defibrillation attempts. Eligibility criteria were assessed at time zero, defined as the first antiarrhythmic drug administration. Inverse probability weighting and logistic regression were used to adjust for confounding variables.
Key Results
The adjusted percentage point difference in survival to hospital discharge favored lidocaine by 2.8% (95% CI, -0.6 to 6.2), with estimated survival probabilities of 26.2% for lidocaine and 23.5% for amiodarone. No significant difference was observed in favorable neurologic outcome at discharge between the lidocaine (17.7%) and amiodarone (16.2%) groups, with an adjusted difference of 1.5% (95% CI, -1.5 to 4.5).
Why It Matters
While no statistically significant difference in survival or neurologic outcome was found, the wide confidence intervals for survival do not exclude a clinically meaningful benefit for lidocaine. These findings align with previous randomized controlled trials, but the authors caution that residual confounding cannot be entirely ruled out.
Score 0.72 · ✓ Verified
Xiang Si et al. — Critical Care · July 17, 2026
Why it’s here: This observational study assesses fluid responsiveness in mechanically ventilated patients with intra-abdominal hypertension, directly relevant to mechanical ventilation and hemodynamic support in the ICU.
Study Design
This prospective study in two ICUs investigated fluid responsiveness in mechanically ventilated patients with (IAH +) and without (IAH-) intra-abdominal hypertension. Patients underwent passive leg raising (PLR), end-expiratory occlusion (EEO), and mini-fluid challenge, with cardiac index changes monitored via transpulmonary thermodilution.
Key Results
The passive leg raising (PLR) test showed reduced accuracy in IAH+ patients (AUROC 0.71) compared to IAH- patients (AUROC 0.96), with impaired PLR response potentially linked to persistently negative CVP-IAP gradients. However, the end-expiratory occlusion (EEO) test (AUROC 0.89 vs. 0.95) and mini-fluid challenge (AUROC 0.90 vs. 0.94) maintained high diagnostic accuracy in both groups.
Why It Matters
In patients with intra-abdominal hypertension, the passive leg raising test's reliability for predicting fluid responsiveness is diminished, possibly due to altered pressure gradients. The end-expiratory occlusion test and mini-fluid challenge emerge as reliable alternatives for assessing fluid responsiveness in this population.
Score 0.62 · ⓘ Reference
Max Melchers, Peter Pickkers, Arthur R. H. van Zanten — Critical Care · July 17, 2026
Why it’s here: This study examines norepinephrine trajectories during vasopressin therapy in septic shock, directly relevant to vasopressor support and sepsis management in the ICU.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.62 · ✓ Verified
Priyanka Hastak et al. — Critical Care · July 17, 2026
Why it’s here: Focuses on critically ill adults with severe acute respiratory infections in the ICU, relevant to ARDS and ICU outcomes.
Study Design
This Australia-wide retrospective observational study analyzed data from a prospective, nation-wide registry of 46 ICUs. It included critically ill adults (age ≥ 18) with laboratory-confirmed viral SARI, comparing immunocompromised patients to immunocompetent ones. Immunocompromised status was defined by specific conditions including chronic immunosuppression, malignancy, AIDS/HIV, or organ transplantation.
Key Results
Among 4,703 patients with viral SARI, 19.3% were immunocompromised and had higher rates of COVID-19 (51.1% vs. 34.5%) and more comorbidities. Immunocompromised patients received more treatments like antivirals (67.3% vs. 57.7%) and corticosteroids (81.7% vs. 72%). They also experienced higher in-hospital mortality (22.7% vs. 13.1%).
Why It Matters
Immunocompromised patients represented approximately one-fifth of critically ill viral SARI patients and faced nearly double the odds of in-hospital mortality compared to immunocompetent individuals. After adjustment, immunocompromised status was independently associated with an adjusted OR of 1.93 for in-hospital mortality. These findings suggest a need for targeted public health campaigns and improved treatment strategies for this vulnerable population.
✦ Something Different · Score 0.57 · ✓ Verified
Feixiang Huo et al. — Critical Care Medicine · July 17, 2026
✦ A change of pace: This article explores a novel combination therapy for tracheostomized patients with neurological disorders, offering a unique perspective on airway clearance and rehabilitation techniques distinct from the more common pharmacological and hemodynamic topics.
Study Design
This randomized, assessor-blinded, controlled trial investigated the effects of abdominal electrical stimulation combined with airway clearance techniques in adult tracheostomized patients with neurologic disorders. The study was conducted in the Department of Rehabilitation Medicine and included 68 patients who had been weaned off mechanical ventilation. Patients received conventional treatment with airway clearance techniques for 6 weeks, with the experimental group additionally receiving abdominal electrical stimulation.
Key Results
The experimental group showed a significantly higher decannulation success rate (93.3%) compared to the control group (73.3%), with an odds ratio of 5.070 (p = 0.013). Significant improvements were also observed in peak involuntary cough flow rate (mean difference, 39.433 L/min; p < 0.001) and abdominal muscle thickness (difference, 0.106 cm; p < 0.001) in the experimental group. No significant differences were found in clinical pulmonary infection scores or resting diaphragm mobility between the groups.
Why It Matters
Adding abdominal electrical stimulation to airway clearance techniques significantly enhances decannulation success and cough efficacy in tracheostomized patients with neurologic disorders. While these findings suggest a beneficial intervention for improving outcomes, the study did not find an effect on infection control or resting diaphragmatic activity. This highlights a specific benefit of the combined approach without altering broader respiratory parameters.
Healthcare machine learning6 articles
Score 0.82 · ✓ Verified
Ahmed Abdelhameed et al. — npj Digital Medicine · July 14, 2026
Why it’s here: This article describes a transformer-based deep learning model for predicting patient-specific ischemic and bleeding risks, directly aligning with the interest in predictive models and clinical care.
Study Design
This study developed Transformer-DAPT, a transformer-based deep learning survival framework, to estimate patient-specific ischemic and bleeding risks over time after PCI. The model was trained on electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium.
Key Results
Transformer-DAPT achieved time-dependent concordance indices (Ctd) of 0.84–0.87 for ischemic events and 0.81–0.88 for bleeding events in the internal cohort, outperforming DeepSurv and DeepHit by 2%–12%. In the external validation cohort, Ctd ranged from 0.74–0.84 for ischemic events and 0.75–0.83 for bleeding events.
Why It Matters
Transformer-DAPT demonstrates improved discrimination and calibration compared to traditional rule-based scores and other deep learning models, offering a framework for multi-interval risk prediction. This approach can support more personalized DAPT management decisions after PCI, moving beyond static, single-time-point estimates.
● Preprint · Score 0.82 · ✓ Verified
Liu, Y. et al. — medRxiv Preprint · intensive care and critical care medicine · July 13, 2026
Why it’s here: This article directly evaluates LLMs for clinical decision support in antibiotic prescribing, aligning perfectly with the researcher's interests.
Study Design
This study employed a double-blind, randomized-sequence evaluation using a 2×2 factorial design to compare a domain-specific LLM (MedGo) and a general-purpose LLM (DeepSeek V3.5) under standard and chain-of-thought (CoT) prompting strategies. The models' performance was assessed across 59 complex inpatient infection cases, with five parallel regimens generated per case and evaluated by three senior clinicians.
Key Results
Real physicians achieved the highest score ranking (4.76), followed by MedGo-CoT (4.58) and DeepSeek-CoT (4.41), with MedGo (4.25) and DeepSeek (3.98) scoring lower. CoT prompting significantly improved both models' scores and reduced score dispersion, with MedGo-CoT outperforming DeepSeek-CoT in individualized adjustment and dosing precision.
Why It Matters
Domain-specific LLMs enhanced with CoT prompting show promise in antibiotic decision-making, approaching the level of real physicians, particularly in individualization and dosing precision. However, limitations in antimicrobial stewardship awareness and automated evaluation reliability highlight the continued necessity of senior clinical expertise.
Score 0.78 · ✓ Verified
Yu Jin et al. — npj Digital Medicine · July 14, 2026
Why it’s here: This article presents a smartphone-based computer vision framework for screening degenerative cervical myelopathy, demonstrating a predictive model for clinical diagnosis using novel data sources.
Study Design
This multicenter study developed a smartphone-based computer vision framework to differentiate degenerative cervical myelopathy (DCM) from age-related functional decline. The framework identified eight demographically robust kinematic digital biomarkers, such as maximum release velocities and inter-finger synchronization, from 2340 participants. These biomarkers serve as objective surrogates for corticospinal integrity, remaining stable despite muscle senescence.
Key Results
The developed model achieved an AUC of 0.896 in the development cohort, significantly outperforming the conventional 20-cycle threshold (AUC 0.768; P < 0.001). In an external validation cohort, the model yielded an AUC of 0.856, with 83.1% sensitivity and 79.9% specificity. This discriminative accuracy remained robust within the diagnostic gray zone.
Why It Matters
This tool offers a scalable and objective solution for DCM risk stratification in aging populations within primary care by focusing on kinematic pattern quality rather than movement quantity. The model's performance in external validation, including a 64.1% PPV and 91.8% NPV at a 30% prevalence, supports its potential application. It addresses the challenge of distinguishing DCM from natural aging-related functional decline, which is critical in primary care.
Score 0.78 · ✓ Verified
Transparent chest radiograph foundation model enables explainable human disease profiling
Chin Lin et al. — npj Digital Medicine · July 16, 2026
Why it’s here: Introduces a chest radiograph foundation model for disease profiling, emphasizing explainability and clinical relevance, aligning with LLMs in medicine and predictive modeling.
Study Design
This study developed a contrastively pretrained multimodal CXR foundation model using large-scale image-report pairs to predict diverse human diseases. The model's capacity was evaluated by training linear probes on frozen image embeddings and validating performance across three independent cohorts totaling over 230,000 patients, using 1074 phecodes derived from electronic health records.
Key Results
The model significantly predicted 554 prevalent and 457 incident phenotypes, with 60 prevalent and 42 incident phenotypes showing consistently high discrimination across all datasets. Co-embedding analysis identified 28 phenotype clusters driven by recognizable imaging patterns and enabled reconstruction of most predictions with strong explanatory performance (R² ≥ 0.85).
Why It Matters
These findings demonstrate that CXR foundation model embeddings encode rich, clinically relevant information beyond conventional interpretation, providing a scalable and interpretable framework for comprehensive disease profiling. The embeddings captured imaging signatures associated with near-term cardiovascular events and critical illness, offering a foundation for future prospective validation.
Score 0.72 · ✓ Verified
Siteng Chen et al. — npj Digital Medicine · July 13, 2026
Why it’s here: This article develops a deep learning model for predicting prognosis and immunotherapy benefit in kidney cancer from pathological images, directly fitting the interest in predictive models and clinical care.
Study Design
This multi-center cohort study developed a deep learning-based CD8+ T cell inflammation signature (DL-CD8T) for clear cell renal cell carcinoma (ccRCC) using hematoxylin and eosin-stained whole slide images. A clustering-constrained attention multiple-instance learning method was employed for model development.
Key Results
The DL-CD8T achieved an AUC of 0.781 in the training cohort and 0.741 in the independent CPTAC cohort for distinguishing patients with higher CD8+ T cell inflammation. DL-CD8T positivity was associated with high CD8+ T cell infiltration, elevated immune checkpoint expression (PD-1, PD-2, PD-L1, CTLA-4, TIM-3), and higher tumor mutation burden, identifying patients with high survival risk.
Why It Matters
The DL-CD8T may improve risk stratification and inform personalized targeted therapy in ccRCC, potentially predicting benefit from combined targeted and immunotherapy with a hazard ratio of 0.27. Pending prospective validation, this digital pathology approach offers a promising tool for ccRCC management.
✦ Something Different · Score 0.54 · ✓ Verified
Vahideh Zolfaghari et al. — npj Digital Medicine · July 14, 2026
✦ A change of pace: This article explores the safety and limitations of AI in responding to pediatric health queries, offering a crucial perspective on AI's role in direct patient interaction and safety, which is distinct from the diagnostic or predictive applications seen in other candidates.
Study Design
This study evaluated the safety boundary maintenance of four consumer AI systems (GPT-4o-mini, Gemini-2.0-Flash, Claude-3.5-Haiku, and Llama-3.1-8B) using PediatricSafetyBench-v2, a benchmark of 600 pediatric health queries. The benchmark included 300 authentic caregiver queries and 300 adversarial variants designed to simulate caregiver pressure patterns.
Key Results
The overall safety-appropriate rate across the four AI systems was 95.5%, with a safety-oriented system prompt deployment improving this rate by 5.9 percentage points. Counter-intuitively, adversarial caregiver pressure was associated with higher Safety Composite Score values for all models, with false expertise claims being the most vulnerability-inducing pattern.
Why It Matters
Consumer AI systems generally maintain safety boundaries in pediatric health interactions, but PediatricSafetyBench-v2 highlights specific pressure patterns that can influence AI responses. The benchmark is publicly released for longitudinal safety monitoring, offering a tool to track AI safety over time.
Hospital logistics and operations5 articles
Score 0.72 · ✓ Verified
A unified multi-modal foundation model for end-to-end emergency care
Zhenwei Huang et al. — npj Digital Medicine · July 14, 2026
Why it’s here: Directly addresses emergency department overcrowding and patient flow using AI, a core hospital operations topic.
Study Design
This study proposes ED-Foundation, a unified emergency foundation model built on the BEIT-3 architecture to address overcrowding in Chinese tertiary hospital emergency departments. The model jointly leverages aligned image-text pairs and unaligned pure-text data with a two-stage self-supervised learning framework to learn continuous patient-centric representations and improve robustness to missing data.
Key Results
ED-Foundation was evaluated on nine downstream validation datasets across early emergency triage, outcome prediction, and clinical decision-making support, consistently achieving state-of-the-art retrospective performance. It outperformed prior task-specific approaches and existing foundation models under limited information and modality missingness in diverse institutional and cross-system settings.
Why It Matters
These results provide retrospective evidence supporting ED-Foundation's benchmark performance and representational transferability as a unified framework for emergency care tasks, offering a potential solution for scalable deployment in resource-heterogeneous real-world settings. The model's robustness to pervasive missingness is a key advantage for practical application.
★ Flagship · Score 0.67 · ✓ Verified
Managing Software Component Quality with Automation: Evidence from Dependabot
Seongkyoon Jeong, Eunae Yoo — Manufacturing & Service Operations Management · July 17, 2026
Why it’s here: This article applies operations research to software component quality management and resolution speed, using concepts like automation and hazard rates, which are transferable to managing operational processes in hospitals.
Study Design
This study investigates the impact of automation on resolving software security vulnerabilities. Using survival analysis on 1,963,957 JavaScript open-source software packages, the research examines how the adoption of Dependabot affects the speed of addressing vulnerable dependencies.
Key Results
Packages that adopted Dependabot showed a 2.499 times higher resolution hazard, resolving vulnerable dependencies 60% faster. However, even with automation, the median resolution time for vulnerable dependencies was 82 days, indicating that automation is not a complete solution.
Why It Matters
The findings demonstrate that automation, like Dependabot, can significantly improve the speed of resolving software component vulnerabilities, but its benefits are bounded by human-driven constraints such as slow code modification processing and compatibility verification. This research offers guidance on leveraging automation while addressing these impediments to enhance software quality.
Score 0.66 · ✓ Verified
Earlier medically ready for discharge date entry associated with lower unnecessary bed days
Keely Dwyer-Matzky, Dessislava A. Pachamanova, Vera Tilson — BMC Health Services Research · July 16, 2026
Why it’s here: Directly addresses hospital bed management, discharge planning, and reducing unnecessary bed days, core hospital operations topics.
Study Design
This study evaluated the implementation of a program requesting healthcare providers to estimate and record the Medically-Ready-for-Discharge Date (MRDD) in the EHR for hospitalized patients. The study analyzed 112,930 patient visits from 4 hospitals between January 2021 and July 2022, with detailed subgroup analysis for 33,771 Medicine and Pediatric patients. Multivariable regression and tree-based machine learning methods were used to identify factors associated with unnecessary bed days (UBD).
Key Results
A longer delay in entering the MRDD forecast after admission was associated with greater UBD, while a shorter time between the first and last MRDD entry was linked to lower UBD. Specifically, a statistically significant decrease in UBD was associated with earlier entry of the MRDD forecast. Disposition, level of care, insurance, and day of week were also significant predictors of UBD.
Why It Matters
Earlier entry of the MRDD forecast was statistically associated with a decrease in unnecessary bed days, which can improve patient throughput in hospitals facing capacity constraints. The findings demonstrate the consequences of implemented policies and quantify their impact on UBD, potentially aiding other hospitals in assessing similar initiatives. The study highlights the role of discharge planning in managing patient length of stay.
Score 0.62 · ✓ Verified
Optimizing vaccine site locations while considering travel inconvenience and public health outcomes
Suyanpeng Zhang et al. — Health Care Management Science · July 13, 2026
Why it’s here: Applies optimization models to vaccine site location and dose allocation, directly relevant to healthcare logistics and planning.
Study Design
This study developed a multi-objective mixed-integer linear programming model to optimize vaccine site locations and dose allocation during the COVID-19 pandemic. The model explicitly incorporated travel inconvenience, disease dynamics, and equitable distribution by considering commuting patterns from both residential and workplace origins.
Key Results
The proposed model recommended more dispersed mega-site locations compared to the empirical solution used in Los Angeles County in 2020. This optimization resulted in a 26% reduction in travel inconvenience and averted an additional 200 infections.
Why It Matters
These findings demonstrate that a jointly optimized approach to site selection and dose allocation can significantly improve efficiency and equity in mass vaccination planning. The model's tractable objective formulation effectively proxies key public health goals, offering a practical tool for future public health interventions.
★ Flagship · Score 0.61 · ✓ Verified
Discretion in Automated Supermarket Replenishment: Censorship Bias and Self-inflicted Stockouts
Bengü Nur Özdemir, Antti Tenhiälä — Manufacturing & Service Operations Management · July 17, 2026
Why it’s here: This article uses operations research concepts like inventory management, stockouts, and decision-making biases in a retail context, which has strong parallels to hospital supply chain and inventory operations.
Study Design
This study analyzes data from an upmarket supermarket chain concerning perishable products to investigate decision-maker behavior in automated store replenishment. The researchers employ a recursive bivariate probit model with exclusion restrictions to address the endogeneity of deviations, complemented by endogenous switching regression and seemingly unrelated regression models for robustness.
Key Results
The findings indicate that decision-makers are more likely to deviate downward from automated replenishment proposals after a stockout, consistent with censorship bias, although anchoring bias is found to be more powerful in predicting these downward deviations. Crucially, censorship bias is shown to be more detrimental, increasing the likelihood of a new stockout compared to anchoring bias.
Why It Matters
Understanding censorship bias is vital for maximizing the benefits of discretionary power in inventory replenishment and can help distinguish uninformed downward deviations from informed ones. By blocking downward deviations suspected to be driven by censorship bias, retail managers can reduce self-inflicted stockouts with manageable inventory cost implications.
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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