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Evidence record 17022 · automatically gathered

Auditing Sex/Gender Disparities in Emergency Triage with LLM-based Paired Comparisons

arXiv:2511.17124v2 Announce Type: replace Abstract: We present a domain-agnostic paired-comparison approach that uses Large Language Models (LLMs) to quantify sex/gender-related asymmetries in documented clinical decision-making. The method trains an LLM to emulate observed decisions, then evaluates sex-swapped pairs in which only sex is flipped, holding documented clinical content constant. We apply it to emergency triage, analyzing more than 140,000 Bordeaux University Hospital (France) admiss

Record details

Published: 7 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Healthcare · Transparency
Retrieved: 7 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 August 2026), “Auditing Sex/Gender Disparities in Emergency Triage with LLM-based Paired Comparisons,” evidence record 17022, https://ethics.ai/record/17022 (originally published by arXiv cs.CY).

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