2026-07-31 04:00 UTCOriginal source2 min readUpdated: 2026-07-31 09:25 UTC

Same Facts, Different Diagnosis: Measuring and Mitigating Narrative Anchoring in Clinical Language Models

Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. A new benchmark of 1,000 USMLE vignettes, each rewritten into three distinct personas, reveals that identical clinical facts produce divergent diagnoses across seven tested models—term this 'narrative anchoring.' Existing debiasing methods only partially help, but a new three-agent pipeline, NarrativeShield, reduces the anchoring gap to near-zero with modest accuracy costs.

SourcearXiv Computational LinguisticsAuthor: Prabhjot Singh, Pritam Deka, Vijay Chennareddy

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Key points

Clinical language models exhibit 'narrative anchoring': identical clinical facts expressed in different sociolinguistic registers yield divergent diagnoses.
Existing debiasing methods such as chain-of-thought and explicit instructions only partially mitigate the bias and often coincide with accuracy collapse.
The proposed NarrativeShield pipeline reduces the narrative anchoring gap to near-zero with only modest accuracy cost for most models.

Why it matters

This matters because clinical language models exhibit 'narrative anchoring': identical clinical facts expressed in different sociolinguistic registers yield divergent diagnoses.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 29 Jul 2026]

Title:Same Facts, Different Diagnosis: Measuring and Mitigating Narrative Anchoring in Clinical Language Models

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Abstract:Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. We term this failure mode Narrative Anchoring: identical clinical facts expressed in different registers cause diagnostic outputs to diverge. Unlike prior demographic-bias work, which manipulates explicit identity tokens such as race or income, our benchmark isolates register as the sole channel of variation, with no demographic marker present in any form. We construct a dataset of 1,000 USMLE clinical vignettes, each rewritten into three sociolinguistically distinct personas under an independently audited fact-preservation guarantee, verified by a separate model that never sees the generation prompt. Across seven language models spanning three architecture families and scales, Narrative Anchoring is statistically significant under direct prompting in every model tested, with a Narrative Anchoring Gap of 0.064 to 0.151. Chain-of-thought reasoning and explicit debiasing instructions reduce the bias only partially, and their apparent gains are frequently confounded by accuracy collapse. We introduce NarrativeShield, a three-agent pipeline that structurally extracts and verifies clinical facts before diagnostic reasoning begins, reducing the Narrative Anchoring Gap to near-zero ($-0.004$ to $0.037$) and achieving the lowest rate of severely unstable decisions (DSS $

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