Jama Benchmark
Jama Benchmark - Related Videos
09:47
Generation of Human Alloantigen-specific T Cells from Peripheral Blood
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Cited by 2 •
2014
This article describes a method for the generation and propagation of human T cell clones that specifically respond to a defined alloantigen. This protocol can be adapted for cloning human T cells specific for a variety of peptide-MHC ligands.
09:41
Concentration Dependence - Student Protocol
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2020
Source: Smaa Koraym at Johns Hopkins University, MD, USA Concentration DependenceExpand In this experiment, you will combine varying concentrations of aqueous HCl and sodium thiosulfate to make solid sulfur, which rapidly gathers into opaque yellow particles at a certain sulfur concentration. Since the reaction solution starts clear and colorless, you can easily tell when it reaches that concentration. You'll use the same test tube and total volume each time, so it will take the same amount...
07:35
How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling
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2026
In the context of the virtual celebrity experience, content quality most strongly drives satisfaction; satisfaction, in turn, increases purchase intention indirectly via loyalty. Personalization strengthens the satisfaction-loyalty link, so firms should prioritize emotion-evoking, narrative-consistent virtual celebrity content and personalized loyalty programs.
14:18
The 4 M’s Framework: Case History and Physical Examination of Older Adults
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2023
Source: Jennifer A. Ouellet and Jaideep Talwalkar; Yale School of Medicine To meet the needs of older adults, all health professionals should be well acquainted with the history and physical examination considerations unique to this population. Physical examination plays an important role in the older patient to detect physiologic changes of aging, risk factors, and signs of pathology. While most of the general principles of the standard examination for adults apply to older patients, there are...
06:02
A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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2025
This protocol provides a streamlined computational pipeline for quantifying nascent enhancer transcripts. By integrating chromatin accessibility, chromatin feature, and transcriptional data, it enables accurate detection and strand-specific analysis of enhancer activity in complex intragenic regions, while remaining accessible to researchers without extensive bioinformatics training.
FAQs
JAMA Benchmark examines more than whether a model produces fluent text. Its evaluation can separately consider accuracy, diagnostic reasoning, consistency, and robustness when responding to clinically relevant cases or questions. This multidimensional view helps determine whether a system reaches expert-aligned conclusions and handles medical tasks reliably, rather than merely generating plausible-sounding language.
A model may answer one medical question correctly yet perform unreliably across related cases or evaluation conditions. Measuring consistency and robustness exposes that instability, while accuracy shows whether responses align with validated answers. Together, these measures provide a stronger indication of whether performance could support evidence-based decision-making and where additional human oversight remains necessary.
Expert-validated answers provide the reference standard against which model responses are compared. This anchors evaluation in clinically reviewed judgments rather than general linguistic quality alone. The comparison allows researchers to identify incorrect or weak diagnostic conclusions, assess the reliability of model reasoning, and interpret results in relation to medically relevant expectations.
An evaluation typically presents the system with a curated set of medical cases or questions, records its responses, and compares those responses with expert-validated answers. Researchers then examine measures such as accuracy, diagnostic reasoning, consistency, and robustness. Using the same structured conditions across systems makes differences in performance easier to interpret.
Results can reveal that a system performs differently across medical specialties or types of clinically relevant tasks. Those patterns help developers locate weaknesses, refine models, and prioritize further evaluation. For researchers, specialty-level findings clarify whether improvements reflect broad medical capability or only stronger performance in selected areas.
Strong benchmark performance indicates alignment with expert-validated answers on the evaluated cases and measures, but it does not remove the need for human judgment. The results can inform safer clinical translation by showing where a system appears reliable and where limitations remain. In practice, benchmark evidence supports careful assessment rather than replacing clinician oversight.