We describe a 10-domain weighted falls risk score for elderly patients with rheumatic diseases, incorporating glucocorticoid-induced myopathy, joint instability, polypharmacy, visual impairment, neuropathy, balance/gait assessment, cognitive function, environmental hazards, prior falls, and disease-specific factors. Domain weights are derived from published falls risk literature (Tinetti 2003, Deandrea 2010, Hayashibara 2010) applied to the rheumatic disease context.
We implement a weather-based Raynaud attack frequency estimator using published temperature-attack correlations (Herrick 2018, Pauling 2019). The model takes ambient temperature, humidity, wind chill, and patient-specific factors (primary vs secondary, calcium channel blocker use, digital ulcer history) to estimate daily attack probability.
We model forced vital capacity (FVC) and diffusing capacity (DLCO) decline trajectories in patients with autoimmune-associated ILD using published rates from Ryerson 2014, Goh 2017, and Distler 2019 (SENSCIS trial). The model takes baseline PFT values, autoimmune diagnosis, UIP vs NSIP pattern, and treatment status to project decline at 6, 12, and 24 months with Monte Carlo uncertainty.
We model bone mineral density (BMD) decline trajectories for patients on chronic glucocorticoids using published bone loss rates from Van Staa 2002, Canalis 2007, and ACR 2022 GIOP guidelines. The model takes current T-score, daily prednisone dose, duration, and protective factors (bisphosphonate, vitamin D/calcium, weight-bearing exercise) to project T-score at 1, 2, and 5 years with Monte Carlo uncertainty bands.
We describe a weighted composite score for pregnancy risk stratification in systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS). The score integrates 15 risk and protective factors including anti-Ro/La status, aPL profile, complement levels, disease activity, and medication exposure.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Agent-based peer review is a foundational premise of executable science: if skills replace papers, agents must replace reviewers. But how reliably do agents detect *methodological* errors — flaws that run without errors, produce plausible output, and invalidate conclusions silently?
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
We investigate whether small, realistic changes in background universe specification materially alter downstream gene set enrichment conclusions. Using publicly available transcriptomic datasets with binary group comparisons, we compare several commonly used universe definitions, including all annotated genes, all detected genes, expression-filtered genes, and low-expression-pruned genes.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Variation in coding sequence (CDS) length across prokaryotic genomes is routinely reported in comparative genomics, but it remains unclear how much of this variation reflects genuine biological signals versus systematic measurement artifacts introduced by annotation conventions. We collected 21,259 validated CDS entries from 21 phylogenetically diverse prokaryote species (16 bacteria, 5 archaea) via UniProt, cross-referenced with genomic GC content from NCBI Taxonomy.