Citations

ABSTRACT Objectives The increasing volume of medical education research necessitates efficient, reliable, and scalable methods for conducting quality appraisals. The Medical Education Research Study Quality Instrument (MERSQI) is a widely used tool, although its manual scoring process remains resource‐intensive. This study evaluated how well large language models (LLMs) appraise medical education research using the MERSQI tool in comparison with human judges. Methods Three LLMs (GPT‐5, Claude Sonnet 4, and Gemini 2.5 Pro) assigned MERSQI domain scores to 1423 medical education research articles. The authors compared AI‐generated scores with human‐generated scores using intraclass correlation coefficients (ICCs) across the six MERSQI domains. They evaluated the agreement between AI‐ and human‐generated MERSQI composite scores using Bland–Altman plots. Results Domain‐level ICC values ranged from fair (0.24) to near perfect (0.81), with the lowest agreement observed in the ‘sampling,’ ‘validity evidence,’ and ‘data analysis’ domains. No single LLM consistently outperformed the others across all domains. Composite score agreement with human ratings was substantial and similar across LLMs (ICC range: 0.65–0.69). GPT‐5 produced slightly lower composite scores than humans, while Claude Sonnet 4 and Gemini 2.5 Pro produced higher scores, with Gemini showing the largest deviation. The Bland–Altman plots for Gemini 2.5 Pro suggested proportional bias, indicating its agreement with human scores varied across the range of study quality. Conclusions These LLMs demonstrated substantial agreement with human raters for MERSQI composite scores, but domain‐level agreement varied. Systematic differences in scoring patterns highlight the need for human oversight and additional calibrations before integrating LLMs into systematic review appraisal workflows.

Background: Although donor and recipient characteristics are used to estimate graft failure risk, they remain limited in predicting early acute rejection. We developed and validated a next-generation sequencing (NGS) assay targeting the pre-transplant immunologic profile to predict early acute rejection following kidney transplantation. Methods: This prospective, international study enrolled 321 kidney transplant participants across 13 sites, forming discovery and validation cohorts. Peripheral blood was collected prior to transplant and at 1, 3, 6, 12, and 24 months post-transplant, with protocol biopsies performed at 3 and 12 months, as well as any indication biopsies. Biopsies were assessed by a blinded central pathologist according to the 2019 Banff criteria. The pre-transplant risk assessment (PTRA™) test to evaluate RNA expression of a 29-gene signature algorithm designed to predict early acute rejection risk was clinically validated utilizing 122 deceased donor kidney transplant recipients. Results: PTRA classified 31 of 122 participants (25%) as high-risk and 91 (75%) as low-risk. There were 9 early acute rejection events in the first 60 days post-transplant: 6/31 (19%) in the high-risk group and 3/91 (3%) in the low-risk group. PTRA discrimination for early acute rejection at 60 days post-transplant yielded an AUC of 0.78 (95% confidence interval [CI]: 64.4 to 91.5), p<0.001. A cut-off of ≤45 was defined as low-risk and >45 as high-risk for early acute rejection within 60 days post-transplant. Sensitivity 0.67 (95% CI: 0.36 to 0.97), specificity 0.78 (95% CI: 0.70 to 0.86), positive predictive value 0.19 (95% CI: 0.05 to 0.33), and negative predictive value 0.97 (95% CI: 0.93 to 1.00). The odds ratio comparing patients identified as high- vs low-risk by PTRA was 7.04 (95% CI: 1.64 to 30.2), p=0.009. Conclusions: This study validates the ability of PTRA to stratify kidney transplant recipients as high- or low-risk for early acute rejection using a pre-transplant transcriptomic profile, with implications for graft health and personalized treatment management.

Abstract A working group of cIMPACT-NOW evaluated the literature on IDH-mutant gliomas for opportunities to improve pathology-based risk stratification and grading, especially at the interface of CNS WHO grades 2 and 3, since therapeutic decisions depend on risk assessment for clinical management. The group also evaluated newly described IDH-mutant glioma subgroups and considered multidisciplinary aspects of therapeutic decision-making. PDGFRA amplification was associated with highly aggressive clinical behavior of IDH-mutant astrocytomas, consistent with CNS WHO grade 4. Other genetic alterations associated with intermediate risk and most consistent with CNS WHO grade 3 included PIK3 mutations; EGFR alteration; MYCN amplification; and specific retinoblastoma pathway alterations, including CDK4, CDK6, and CCND2 amplification, RB1 homozygous deletion or mutation, and mutation of CDKN2A. DNA methylation signatures of G-CIMP-low or A_IDH_HG were consistent with CNS WHO grade 3 or 4, depending upon other grading criteria. We suggest a mitotic count of ≥3 per 2.4 mm2 could be considered as a CNS WHO grade 3 criterion. Our review did not uncover features to improve the grading of oligodendroglioma, IDH-mutant and 1p/19q-codeleted, although CNS WHO grade 3 tumors with elevated mitotic rates, yet lacking necrosis, microvascular proliferation, CDKN2A/B homozygous deletion, and MRI contrast enhancement, may be associated with extended survival. Implementing evidence-based criteria for risk stratification and grading will improve guidance for clinical decision-making.

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk assessment after a prostate cancer diagnosis and for the care of patients with localized, regional, recurrent, and metastatic disease. These NCCN Guidelines Insights summarize the panel’s discussions on select 2026 updates to the guidelines regarding nonmetastatic disease: the elimination of the very-low-risk group, updates to the Principles of Active Surveillance, and caution in the use of focal therapy in newly diagnosed patients.

Abstract Rationale Quantitative CT using multiple instance learning (MIL)-usual interstitial pneumonia (UIP) classifier and data-driven textural analysis (DTA) provides objective and reproducible measurements of disease pattern and extent, respectively. However, the understanding of their individual or combined relationships with outcomes in fibrotic hypersensitivity pneumonitis (fHP) remains limited. Objectives To determine whether visual CT patterns of fHP or UIP, a MIL-UIP classifier, baseline and changes in DTA over 12-months and DTA at 12 months are associated with progression-free survival (PFS) in fHP. Methods The primary cohort included 134 participants with fHP. Associations between CT variables and subsequent lung function change, and PFS were assessed using linear mixed models and Cox hazards models. Results were validated in a combined dataset from three independent populations (N = 216). Results Visual patterns of fHP or UIP were not associated with PFS. A baseline MIL-UIP classification and DTA were significantly associated with 24-month PFS. However, the MIL-UIP did not predict PFS after adjusting for DTA fibrosis extent. Over 12 months, a 1-point increase in DTA across and within patients resulted in a significant decline in FVC% and DLCO%. A 12-month change in DTA (HR = 1.07, 95%CI 1.01-1.13) and DTA at 12-months (HR = 1.03, 95%CI 1.01-1.05) were associated with subsequent 12-month PFS. Similar estimates were observed in the validation cohort. Conclusions In fHP, the objectively measured extent of lung fibrosis is more prognostic than either a UIP classifier or visual CT patterns of fHP or UIP. Baseline DTA, DTA at 12 months, and its change over 12-months are associated with PFS.

Antiretroviral therapy has transformed treatment of HIV infection from a fatal disease into a chronic condition, yet opportunistic infections remain prevalent in patients with declining CD4 cell counts, requiring a pattern-based radiologic approach and vigilant clinical assessment to differentiate infections, malignancies, and immune reconstitution inflammatory syndrome.