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Results for “Transfer penalty”

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PubMed2026

Interhospital transfer and outcomes after robotic emergency general surgery: a national analysis.

The outcomes of patients transferred to receiving centers who subsequently undergo robotic EGS remain uncharacterized at a national level. We aimed to quantify the association between transfer and outcomes among adults undergoing robotic EGS. We performed a retrospective cohort study of the Nationwide Readmissions Database (2016-2019) including adult nonelective admissions undergoing robotic EGS. Interhospital transfer versus direct admission was the exposure. Survey-weighted logistic regression estimated adjusted odds ratios (aOR) for clinical outcomes; generalized linear models with gamma family and log link estimated adjusted mean ratios (aMR) for length of stay (LOS) and cost. Average marginal effects provided adjusted risks/means and absolute differences. Among 26,869 unweighted robotic EGS admissions, representing an estimated 46,517 admissions nationally, 246 unweighted admissions were interhospital transfers, representing an estimated 444 transfers (1.0%) nationally. Transfers were older, more comorbid, and more severely ill and were treated predominantly at large, teaching hospitals. After adjustment, transfer was associated with a higher risk of postprocedural complications (8.0% vs. 3.5%; aRR 2.26, 95% CI 1.25-3.27), non-home discharge (31.2% vs. 18.9%; aRR 1.65, 95% CI 1.38-1.92), longer LOS (11.49 vs. 5.53 days; AMR 2.08, 95% CI 1.78-2.42), and higher cost ($43,340 vs. $21,821; AMR 1.99, 95% CI 1.68-2.35). The association with postprocedural complications was attenuated after additional adjustment for APR-DRG Severity of Illness, whereas associations with non-home discharge, LOS, and cost persisted. Among patients undergoing robotic EGS, interhospital transfer is independently associated with higher complication burden and greater resource use. Transferred patients represent a small but distinctly high-risk subgroup whose worse outcomes may reflect drivers that extend beyond the choice of surgical approach.

Humans↗

PubMed2026

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗