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Background: Specimens contaminated with intravenous (IV) fluids are common in clinical laboratories. Current methods for detecting contamination rely on insensitive and workflow-disrupting delta checks or manual technologist review. Herein, we assessed the utility of large language models for detecting contamination by IV crystalloids and compared its performance to multiple, but variably trained healthcare personnel (HCP). Methods: Contamination of basic metabolic panels was simulated using 0.9% normal saline (NS), with (n = 30) and without (n = 30) 5% dextrose (D5NS), at mixture ratios of 0.10 and 0.25. A multimodal language model (GPT-4) and a diverse panel of 8 HCP were asked to adjudicate between real and contaminated results. Classification performance, mixture quantification, and confidence was compared by Wilcoxon rank sum. Results: The 95% CIs for accuracy were 0.57–0.71 vs 0.73–0.80 for GPT-4 and HCP, respectively, on the NS set and 0.57–0.57 vs 0.73–0.80 on the D5NS set. HCP overestimated severity of contamination in the 0.10 mixture group (95% CI of estimate error, 0.05–0.20) for both fluids, while GPT-4 markedly overestimated the D5NS mixture at both ratios (0.16–0.33 for NS, 0.11–0.35 for D5NS). There was no correlation between reported confidence and likelihood of a correct classification. Conclusions: GPT-4 is less accurate than trained HCP for detecting IV fluid contamination of basic metabolic panel results. However, trained individuals were imperfect at identifying contaminated specimens implying the need for novel, automated tools for its detection. [ABSTRACT FROM AUTHOR]

Objectives:  Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial., Methods:  Forty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards ( N  = 22) or the guided app ( N  = 24). The primary outcome was successful return of spontaneous circulation (ROSC). Secondary outcomes included code leader stress and confidence, AHA ACLS guideline adherence, and errors. A focus group of 22 residents provided feedback. Statistical analysis included two-sided t -tests and Fisher's exact tests., Results:  App users showed significantly higher ROSC rate (50 vs. 18%; p  = 0.024), correct thrombolytic administration (54 vs. 23%; p  = 0.029), backboard use (96 vs. 27%; p  < 0.001), end-tidal CO2 monitoring (58 vs. 27%; p  = 0.033), and confidence compared with baseline (1.0 vs 0.3; p  = 0.005) compared with controls. A focus group of 22 residents indicated unanimous willingness to use the app, with 82% preferring it over AHA pocket cards., Conclusion:  Our guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice., Competing Interests: J.C. and A.C. are co-creators of the AHA ACLS mobile app (iOS and Android) in collaboration with the American Heart Association., (Thieme. All rights reserved.)

In Caenorhabditis elegans, germline expression programs are actively repressed in somatic tissue by components of the synMuv (synthetic multi-vulva) B chromatin remodeling complex, which include homologs of tumor suppressors Retinoblastoma (Rb/LIN-35) and Malignant Brain Tumor (MBT/LIN-61). However, the full scope of pathways that suppress germline expression in the soma is unknown. To address this, we performed a mutagenesis and screened for somatic expression of GFP-tagged PGL-1, a core P-granule nucleating protein. Eight alleles were isolated from 4000 haploid genomes. Five of these alleles exhibit a synMuv phenotype, whereas the remaining three were identified as hypomorphic alleles of known synMuv B genes, lin-13 and dpl-1. These findings suggest that most suppressors of germline programs in the soma of C. elegans are either required for viability or function through synMuv B chromatin regulation., (Copyright © 2015 Kelly et al.)