Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes

Isotta Landi; Eugenia Alleva; Nicole Bussola; Rebecca M. Cohen; Sarah Nowlin; Leslee J. Shaw; Alexander W. Charney; Kimberly B. Glazer

cs.CLFeb 16, 2026

Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes

Authors: Isotta Landi, Eugenia Alleva, Nicole Bussola, Rebecca M. Cohen, Sarah Nowlin, Leslee J. Shaw, Alexander W. Charney, Kimberly B. Glazer

Organizations: Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY. · Icahn School of Medicine at Mount Sinai, NY. · Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY. · Obstetrics, Gynecology and Reproductive Science, Blavatnik Family Women’s Health Research Institute, Icahn School of Medicine at Mount Sinai, NY. ·

Abstract

Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as stigmatizing, privileging, or neutral. We constructed a curated lexicon of biased terms scored for emotional valence. We then used lexicon-based matching to extract text chunks from OB-GYN delivery notes (Mount Sinai Hospital, NY) and MIMIC-IV discharge summaries across multiple specialties. Three clinicians annotated all chunks, enabling characterization of valence patterns across specialties and healthcare systems. We benchmarked multiple classification strategies (zero-shot prompting, in-context learning, and supervised fine-tuning) across encoder-only models (GatorTron) and generative large language models (Llama). Fine-tuning with lexically primed inputs consistently outperformed prompting approaches. GatorTron achieved an F1 score of 0.96 on the OB-GYN test set, outperforming larger generative models while requiring minimal prompt engineering and fewer computational resources. External validation on MIMIC-IV revealed limited cross-domain generalizability (F1 < 0.70, 44% drop). Training on the broader MIMIC-IV dataset improved generalizability when testing on OB-GYN (F1 = 0.71, 11% drop), but at the cost of reduced precision. Our findings demonstrate that fine-tuning outperforms prompting for emotional valence classification and that models must be adapted to specific medical specialties to achieve clinically appropriate performance. The same terms can carry different emotional valences across specialties: words with clinical meaning in one context may be stigmatizing in another. For bias detection, where misclassification risks undermining clinician trust or perpetuating patient harm, specialty-specific fine-tuning is essential to capture these semantic shifts. * Equal contribution.

Figures & tables

Figure 1: Multi-stage methodology outline.

Figure 2: Mean lexicon scores for stigmatizing and privileging valence are shown alongside emotional valence annotation labels for OB-GYN (panel a) and MIMIC-IV (panel b) text chunks. Solid dots represent annotated valence labels, while vertical lines indicate the range of emotional valences observed in clinical note chunks relative to the original lexicon scores. Blue dots denote instances labeled as neutral during real-world annotation. Solid black dots represent words that were not assigned a stigmatizing or privileging score in the lexicon but were labeled as such during annotation of real-world text chunks.

Model	Input	Training Strategy	OB-GYN		MIMIC-IV
Model	Input	Training Strategy	Dev	Test	MIMIC-IV
GatorTron	Anchored	Standard FT	$0.9531$	$0.9456\ (0.9069,0.9755)$	$\mathbf{0.6765}\ (0.6133,0.7323)$
	Anchored	Prompt FT	$0.9635$	$0.9487\ (0.9117,0.9794)$	$0.5918\ (0.5281,0.6560)$
	LP	Prompt FT	$0.9783$	$\mathbf{0.9572}\ (0.9250,0.9839)$	$0.6598\ (0.5954,0.7210)$
BERT	Anchored	Standard FT	$0.9510$	$0.9154\ (0.8695,0.9540)$	$0.5910\ (0.5233,0.6572)$
	Anchored	Prompt FT	$0.9581$	$0.9311\ (0.8883,0.9665)$	$0.5655\ (0.4967,0.6341)$

Table 1:

F_{1}

scores across different input types and tuning strategies. Numbers in bold represent the highest scores per group of models (encoder-only vs decoder-only) and datasets. Mean and

95\%

confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Valence	Word(s)	Example
Privileging	engaged, engagement, engages	Patient is highly engaged in their care.
	pleasant	Patient was pleasant throughout encounter.
	compliant	Patient is compliant with all recommendations.
Stigmatizing	noncompliant, noncompliance,	…has been noncompliant with follow up.
	poor compliance	Noncompliant with treatment recommendations.
	lazy	Patient seems too lazy to make lifestyle changes.

Appendix

Table 2: Examples of lexicon and sentences for stigmatizing and privileging valence scoring.

Dataset	Annotation	Text Chunk
OB-GYN	Neutral	…Mrs. ___ is unwilling to divulge the patient’s psychiatric history…
	Privileging	…PO intake gradually increasing; more willing to take PO psychiatric medications…
	Stigmatizing	…Pt is unwilling to continue her IOL She feels that she has been in pain since hours prior and wants to be delivered…
MIMIC-IV	Neutral	…You are aware that there are food services such as Wheels on Meals, if your father is willing …
	Privileging	…Upon arrival to the floor, the patient is pleasant and in no distress…
	Stigmatizing	…He has been unwilling to eat or get out of bed lately as per the daughter…

Appendix

Table 3: Clinical note chunks labeled as neutral, privileging, or stigmatizing from the OB-GYN and MIMIC-IV datasets.

Method	Word(s)	Target Class
Single-Word	negative	stigmatizing
	positive	privileging
	neutral	neutral
Multi-Word	negative, stigma, bad	stigmatizing
	positive, privilege, good	privileging
	neutral, instruction, clinical	neutral

Appendix

Table 4: Single- and multi-word verbalizer mappings.

Input	Tuning Strategy	Writer	Instruction
Anchored chunks	Zero-shot	Human	Classify whether the chunk extracted from a clinical note carries a privileging, stigmatizing, or neutral valence.
LP chunks	Zero-shot	Human	Classify whether the word indicated at the beginning of the chunk extracted from a clinical note carries a privileging, stigmatizing, or neutral valence in the context provided.
LP chunks	MIPROv2	Llama 3.3-70B	1. Analyze the given clinical note chunk, focusing on the word indicated at the beginning, and classify its valence as privileging, stigmatizing, or neutral based on the context provided. Consider the emotional tone conveyed by the language used in the chunk, taking into account the medical terminology, patient description, and overall sentiment expressed. Assign a valence that accurately reflects the emotional connotation of the word within the clinical note, ensuring that your classification is informed by the nuances of medical communication and the specific details presented in the chunk.
			2. Analyze the given chunk of text from a clinical note, focusing on the word indicated at the beginning. Determine whether this word conveys a privileging, stigmatizing, or neutral valence within the provided context, and classify it accordingly.
LP chunks	MIPROv2	Med42-70B	1. For each identified keyword in a clinical note chunk (e.g., ”pleasant,” ”adherence”), determine its valence as privileging, stigmatizing, or neutral by considering both medical context and linguistic cues. Implement domain-specific sentiment analysis rules or train a machine learning model on annotated datasets to assign accurate valences. Ensure the instruction emphasizes capturing nuanced expressions typical in clinical documentation while prioritizing factual representations over emotional bias.
			2. In a critical care setting, accurately classify sentiment valence (privileging, stigmatizing, or neutral) in clinical text chunks to inform timely interventions and patient summaries. Given phrases from psychiatric and internal medicine notes, such as ”difficulty inspiring” for respiratory symptoms or ”appropriately dressed” for mental status evaluation, the Language Model must assign correct valence labels considering domain-specific context.

Appendix

Table 5: Instructions for generative tasks with large language models. Optimized instructions are derived from tuning the prompts on (1) OB-GYN; and (2) MIMIC-IV datasets.

Model	Input	Training Strategy	OB-GYN		MIMIC-IV
Model	Input	Training Strategy	Dev	Test	MIMIC-IV
GatorTron	Anchored	Standard FT	$0.9481$	$0.9361\ (0.8892,0.9720)$	$0.6704\ (0.5986,0.7348)$
	Anchored	Prompt FT	$0.9577$	$0.9388\ (0.8944,0.9762)$	$0.5998\ (0.5354,0.6671)$
	LP	Prompt FT	$0.9745$	$0.9503\ (0.9111,0.9818)$	$0.6480\ (0.5824,0.7155)$
BERT	Anchored	Standard FT	$0.9520$	$0.9193\ (0.8714,0.9599)$	$0.5982\ (0.5256,0.6700)$
	Anchored	Prompt FT	$0.9481$	$0.9172\ (0.8683,0.9603)$	$0.5618\ (0.4935,0.6356)$

Appendix

Table 6: Precision scores across different input types and tuning strategies. Mean and

95\%

confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Model	Input	Training Strategy	OB-GYN		MIMIC-IV
Model	Input	Training Strategy	Dev	Test	MIMIC-IV
GatorTron	Anchored	Standard FT	$0.9585$	$0.9576\ (0.9245,0.9817)$	$0.7115\ (0.6547,0.7654)$
	Anchored	Prompt FT	$0.9699$	$0.9637\ (0.9365,0.9855)$	$0.6042\ (0.5371,0.6727)$
	LP	Prompt FT	$0.9823$	$0.9662\ (0.9384,0.9885)$	$0.6808\ (0.6138,0.7431)$
BERT	Anchored	Standard FT	$0.9502$	$0.9132\ (0.8624,0.9526)$	$0.6016\ (0.5319,0.6673)$
	Anchored	Prompt FT	$0.9699$	$0.9504\ (0.9156,0.9777)$	$0.5852\ (0.5164,0.6529)$

Appendix

Table 7: Recall scores across different input types and tuning strategies. Mean and

95\%

confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Model	Input	Training Strategy	MIMIC-IV		OB-GYN
Model	Input	Training Strategy	Dev	Test	OB-GYN
GatorTron	Anchored	Standard FT	$0.6168$	$0.5949\ (0.4187,0.7640)$	$0.5901\ (0.5554,0.6247)$
	Anchored	Prompt FT	$0.7532$	$0.8034\ (0.6482,0.9136)$	$0.7144\ (0.6843,0.7455)$
	LP	Prompt FT	$0.9381$	$0.7189\ (0.5477,0.8619)$	$0.6547\ (0.6225,0.6853)$
BERT	Anchored	Standard FT	$0.5166$	$0.5190\ (0.3552,0.6863)$	$0.4773\ (0.4452,0.5102)$
	Anchored	Prompt FT	$0.7315$	$0.7786\ (0.6249,0.9010)$	$0.6201\ (0.5859,0.6521)$

Appendix

Table 8:

F_{1}

scores across different input types and tuning strategies when training on MIMIC-IV and testing on OB-GYN datasets. Mean and 95% confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Model	Input	Training Strategy	MIMIC-IV		OB-GYN
Model	Input	Training Strategy	Dev	Test	OB-GYN
GatorTron	Anchored	Standard FT	$0.6741$	$0.6147\ (0.4324,0.8046)$	$0.6839\ (0.6452,0.7230)$
	Anchored	Prompt FT	$0.7468$	$0.7755\ (0.6258,0.9020)$	$0.7748\ (0.7461,0.8022)$
	LP	Prompt FT	$0.9484$	$0.7185\ (0.5555,0.8693)$	$0.7680\ (0.7399,0.7957)$
BERT	Anchored	Standard FT	$0.5748$	$0.5306\ (0.3659,0.7089)$	$0.5660\ (0.5299,0.6029)$
	Anchored	Prompt FT	$0.7349$	$0.7998\ (0.6227,0.9350)$	$0.7224\ (0.6924,0.7516)$

Appendix

Table 9: Precision scores across different input types and tuning strategies when training on MIMIC-IV and testing on OB-GYN datasets. Mean and 95% confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Model	Input	Training Strategy	MIMIC-IV		OB-GYN
Model	Input	Training Strategy	Dev	Test	OB-GYN
GatorTron	Anchored	Standard FT	$0.6278$	$0.6027\ (0.4271,0.7927)$	$0.5662\ (0.5353,0.5961)$
	Anchored	Prompt FT	$0.7722$	$0.8699\ (0.7756,0.9482)$	$0.6931\ (0.6617,0.7257)$
	LP	Prompt FT	$0.9308$	$0.7428\ (0.5679,0.8926)$	$0.6328\ (0.6017,0.6627)$
BERT	Anchored	Standard FT	$0.4884$	$0.5309\ (0.3629,0.7100)$	$0.4790\ (0.4502,0.5076)$
	Anchored	Prompt FT	$0.7350$	$0.7784\ (0.6049,0.9078)$	$0.6075\ (0.5759,0.6384)$

Appendix

Table 10: Recall scores across different input types and tuning strategies when training on MIMIC-IV and testing on OB-GYN datasets. Mean and 95% confidence intervals obtained via bootstrapping on the test and external validation datasets are reported.

Clinical NLP

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May 17, 2026J/K move · Enter open · S save

Artificial Intolerance: Stigmatizing Language in Clinical Documentation Skews Large Language Model Decision-Making

Jen-tse Huang, Didi Zhou, Faith Kamau, Amy Oh+4

1Johns Hopkins University.

Social Bias in Language Models Clinical Language Model Evaluation Language Model Robustness

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Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation. However, the robustness of these models against subtle linguistic variations, specifically stigmatizing language (SL) commonly found in human-authored clinical notes, remains critically under-explored. In this work, we investigate whether frontier LLMs inherit and propagate this human bias when processing clinical text. We systematically evaluate nine frontier LLMs across four stigmatized medical conditions, utilizing clinical vignettes injected with varying intensities and phenotypes of SL (doubt, blame, and maligning). Our results demonstrate that all evaluated models exhibit substantial bias, with clinical decision-making significantly skewed towards less aggressive patient management. Notably, we observe a high sensitivity to linguistic framing, where a single SL sentence is sufficient to alter model outputs, revealing a clear dose-response relationship. Furthermore, we evaluate standard prompt-based mitigation strategies, including Chain-of-Thought (CoT) reasoning and model self-debiasing. These approaches show limited efficacy; models struggle to explicitly identify SL while remaining implicitly influenced by it. Our findings expose a critical vulnerability in current LLMs regarding fairness and robustness in clinical NLP, underscoring the need for rigorous algorithmic guardrails to prevent the automation of health disparities.

Abstract

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