Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities

Alexander W. Charney; Alissa A. Valentine; Donald Apakama; Isotta Landi; Lauren A. Lepow; Lili Chan

arxiv: 2509.02651 · v3 · submitted 2025-09-02 · 🧬 q-bio.OT · cs.LG

Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities

Alissa A. Valentine , Lauren A. Lepow , Donald Apakama , Lili Chan , Alexander W. Charney , Isotta Landi This is my paper

Pith reviewed 2026-05-18 19:54 UTC · model grok-4.3

classification 🧬 q-bio.OT cs.LG

keywords bias detectionemergency psychiatryschizophrenia diagnosisnegative sentence ratioracial disparitieslarge language modelspsychiatric notesdiagnostic disparities

0 comments

The pith

A higher ratio of negative sentences in psychiatric notes raises the odds of a schizophrenia diagnosis and reduces the apparent impact of patient race on that outcome.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates how negative language used in emergency department psychiatric notes relates to which patients receive a schizophrenia diagnosis. Researchers analyzed notes from nearly thirty thousand patients using an AI model to measure the proportion of negative sentences and then modeled the odds of schizophrenia while adjusting for demographics and other factors. They report that notes with more negative sentences strongly predict higher odds of schizophrenia and that this measure lessens the direct association with race. Black men whose notes contained high negative content faced the greatest odds. This matters for addressing potential bias in high-pressure settings where first diagnoses often occur.

Core claim

Clinician bias exposure, measured as the negative sentence ratio in psychiatric notes labeled by the Mistral large language model, significantly increases the odds of a schizophrenia diagnosis. When included in the model, this ratio attenuates the effect of patient race, with Black male patients showing the highest odds when their notes have high negative sentence ratios. The findings indicate that sentiment-based metrics from real-world clinical data can identify diagnostic disparities beyond those explained by race or ethnicity alone.

What carries the argument

Negative sentence ratio (NSR), defined as the proportion of sentences labeled negative by the Mistral large language model in emergency psychiatric notes, serving as a quantitative proxy for clinician bias exposure.

If this is right

Patients with notes containing a high proportion of negative sentences are more likely to be diagnosed with schizophrenia rather than anxiety, bipolar, depression, or trauma-related disorders.
Accounting for negative sentence ratio diminishes the statistical association between Black race and schizophrenia diagnosis.
Black male patients with high negative sentence ratios exhibit the greatest odds of receiving a schizophrenia diagnosis.
Language patterns in clinical documentation can be used to detect and potentially address disparities in psychiatric diagnosis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the negative sentence ratio captures bias, interventions aimed at changing how clinicians phrase notes could lower inappropriate schizophrenia diagnoses.
Electronic health record systems might incorporate automated sentiment checks to alert providers to potentially biased documentation.
Similar language-based metrics could be applied in other high-stakes medical decisions where subjective judgment plays a role.
Future studies could test whether reducing negative language in notes leads to changes in diagnostic rates across demographic groups.

Load-bearing premise

The negative sentence ratio identified by the language model measures the clinician's biased perception of the patient rather than the severity of the patient's symptoms or the style of note writing.

What would settle it

A direct comparison of the LLM-assigned negative labels against ratings by human clinicians on the same set of notes, followed by re-running the logistic regression with the human labels.

read the original abstract

The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/ethnic groups to obtain their first schizophrenia (SCZ) diagnosis in the ED, a highly stigmatizing disorder. Therefore, understanding the link between clinician bias exposure and psychiatric outcomes is critical for promoting nondiscriminatory decision-making in the ED. This study examines the association between clinician bias exposure and psychiatric diagnosis using a sample of patients with anxiety, bipolar, depression, trauma, and SCZ diagnoses (N=29,005) from a diverse, large medical center. Clinician bias exposure was quantified as the ratio of negative to total number of sentences in psychiatric notes, labeled using a large language model (Mistral). We utilized logistic regression to predict SCZ diagnosis when controlling for patient demographics, risk factors, and negative sentence ratio (NSR). A high NSR significantly increased one's odds of obtaining a SCZ diagnosis and attenuated the effects of patient race. Black male patients with high NSR had the highest odds of being diagnosed with SCZ. Our findings suggest sentiment-based metrics can operationalize clinician bias exposure with real world data and reveal disparities beyond race or ethnicity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Paper links Mistral-labeled negative sentence ratio in ED notes to higher SCZ odds and attenuated race effects, but the unvalidated labels likely track symptom content rather than clinician bias.

read the letter

The main thing to know is that this paper reports a concrete association: higher negative sentence ratios in psychiatric notes, scored by Mistral, raise the odds of a schizophrenia diagnosis in a large ED sample and appear to explain away some of the racial disparity in those diagnoses. Black male patients with high NSR show the highest odds after controls for demographics and risk factors. They use a real dataset of 29,005 patients and straightforward logistic regression, which is appropriate for the observational setup. The specific application of LLM sentiment labeling to operationalize bias exposure in this context is new relative to the cited work, and the scale plus the focus on ED notes give it practical appeal for anyone thinking about scalable signals from clinical text. The regression treats NSR as an independent predictor rather than fitting it directly to the outcome, so it avoids obvious tautology on the paper's own terms. The soft spot sits at the interpretation step. SCZ notes routinely describe hallucinations, paranoia, or agitation, and an off-the-shelf LLM will flag those sentences negative regardless of clinician attitude. The paper gives no human validation, error analysis, or sensitivity checks on the Mistral labels, so NSR could easily reflect patient presentation or note-writing style instead of bias exposure. The controls for listed risk factors do not address the textual content that generates the predictor. This is a real limitation, not a minor one, because the bias claim rests on it. The work is for researchers in clinical NLP, health disparities, or psychiatric services who want to explore language-based metrics on large note corpora. A reader already working on bias detection would get usable ideas and a clear empirical result to build on, though they would add validation layers themselves. It deserves peer review because the data and question are substantive and the methods are simple enough for referees to give targeted feedback on the labeling gap.

Referee Report

3 major / 2 minor

Summary. The paper claims that clinician bias exposure—operationalized as the negative sentence ratio (NSR) in emergency department psychiatric notes labeled by the Mistral LLM—is associated with higher odds of a schizophrenia (SCZ) diagnosis after logistic regression adjustment for patient demographics and risk factors. High NSR attenuates race effects, with Black male patients showing the highest odds; the authors conclude that sentiment-based metrics can quantify bias exposure and reveal disparities beyond race or ethnicity in a sample of N=29,005 patients with anxiety, bipolar, depression, trauma, and SCZ diagnoses.

Significance. If the NSR metric validly indexes clinician bias exposure rather than symptom severity or documentation differences, the work would supply a scalable, data-driven approach to measuring bias in real-world clinical notes and link it to diagnostic outcomes. The large observational sample, use of pre-trained LLM without outcome-fitting, and inclusion of demographic/risk-factor controls are strengths that support the observational design. Credit is given for the parameter-free computation of NSR directly from notes and the attempt to move beyond race-only disparity analyses.

major comments (3)

[Methods (LLM labeling)] Methods section on LLM labeling and NSR computation: no human validation, inter-annotator agreement, accuracy metrics, or error analysis is reported for Mistral's negative-sentence classification on psychiatric notes. This is load-bearing for the central claim because SCZ notes routinely contain descriptions of hallucinations, paranoia, or agitation that an LLM sentiment model will flag as negative irrespective of clinician attitude; without validation, NSR cannot be interpreted as clinician bias exposure.
[Results (logistic regression)] Results section on logistic regression and high-NSR subgroup: the threshold defining 'high' NSR is a free parameter with no sensitivity analysis, reported error bars on odds ratios, or robustness checks across alternative cutoffs. This undermines the specific claim that high NSR attenuates race effects and produces the highest odds for Black male patients.
[Discussion] Discussion of regression interpretation: the model adjusts for listed risk factors and demographics but cannot adjust for the textual content that directly produces the NSR predictor. This leaves open whether the reported association reflects clinician bias or systematic differences in note style and symptom documentation by diagnosis.

minor comments (2)

[Abstract/Methods] Abstract and Methods: the exact sentence-splitting procedure and the LLM prompt used to label negativity are not detailed, making the NSR metric difficult to reproduce.
[Results] Table or figure reporting odds ratios: confidence intervals or standard errors should be added to all reported effects to allow assessment of precision.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important considerations for interpreting our findings on NSR as a proxy for clinician bias exposure. We address each major point below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

Referee: Methods section on LLM labeling and NSR computation: no human validation, inter-annotator agreement, accuracy metrics, or error analysis is reported for Mistral's negative-sentence classification on psychiatric notes. This is load-bearing for the central claim because SCZ notes routinely contain descriptions of hallucinations, paranoia, or agitation that an LLM sentiment model will flag as negative irrespective of clinician attitude; without validation, NSR cannot be interpreted as clinician bias exposure.

Authors: We agree that the absence of direct validation for Mistral's sentence-level classifications on psychiatric notes limits the strength of the bias-exposure interpretation. In the revised manuscript we will add a dedicated limitations subsection that discusses this issue explicitly, including the possibility that negative language in SCZ notes may partly reflect symptom content. We will also report a post-hoc human validation exercise on a random sample of 200 sentences (stratified by diagnosis) to quantify agreement between Mistral labels and clinician annotators, along with error analysis of discordant cases. revision: yes
Referee: Results section on logistic regression and high-NSR subgroup: the threshold defining 'high' NSR is a free parameter with no sensitivity analysis, reported error bars on odds ratios, or robustness checks across alternative cutoffs. This undermines the specific claim that high NSR attenuates race effects and produces the highest odds for Black male patients.

Authors: The referee correctly notes that the binary 'high NSR' threshold was not subjected to sensitivity testing in the original submission. We will revise the Results section to present the primary models using continuous NSR as well as multiple binary thresholds (median, 75th percentile, and 90th percentile). All logistic regression tables will include 95% confidence intervals for odds ratios, and we will add a supplementary figure showing how the race-by-NSR interaction and the Black-male elevation change across these specifications. revision: yes
Referee: Discussion of regression interpretation: the model adjusts for listed risk factors and demographics but cannot adjust for the textual content that directly produces the NSR predictor. This leaves open whether the reported association reflects clinician bias or systematic differences in note style and symptom documentation by diagnosis.

Authors: We concur that residual confounding by unmeasured textual features is a substantive limitation. The revised Discussion will explicitly acknowledge that NSR is computed from the same notes that contain diagnostic descriptors, and therefore the observed association may partly capture differences in symptom severity or documentation style rather than bias alone. We will reframe the conclusions to emphasize that the findings are observational associations and will propose future studies that could separate these mechanisms (e.g., blinded note review or prospective designs). revision: yes

Circularity Check

0 steps flagged

No circularity: NSR computed independently from notes via pre-trained LLM, then used as predictor

full rationale

The paper computes the negative sentence ratio (NSR) directly as the ratio of Mistral-labeled negative sentences to total sentences in the psychiatric notes. This NSR is then entered as an independent covariate in logistic regression to model SCZ diagnosis odds, controlling for demographics and risk factors. Because NSR is extracted from the raw textual input without any fitting, optimization, or definitional dependence on the SCZ outcome variable, the reported associations do not reduce to a tautology or self-fulfilling prediction by the paper's own equations. No self-citations, uniqueness theorems, or ansatzes are load-bearing for the central claim, and the analysis relies on external patient records and an off-the-shelf LLM. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim depends on the validity of the LLM as a bias proxy and on standard logistic regression assumptions; no new physical or mathematical entities are introduced.

free parameters (1)

NSR threshold for 'high'
The paper refers to 'high NSR' without stating the exact cutoff value used in the interaction analysis.

axioms (2)

standard math Logistic regression coefficients can be interpreted as odds ratios after controlling for listed covariates
Invoked when stating that high NSR increases odds and attenuates race effects.
domain assumption Mistral LLM labels of negative sentences accurately reflect clinician bias exposure
Central to treating NSR as a bias metric rather than a documentation artifact.

pith-pipeline@v0.9.0 · 5772 in / 1423 out tokens · 38771 ms · 2026-05-18T19:54:57.929394+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Negative Sentence Ratio (NSR) = N_NEGATIVE / N_SENTENCES; logistic regression SCZ ~ ... + NSR + Race*Sex*SES interactions

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Heather Hall. Dissociation and misdiagnosis of schizophrenia in populations experiencing chronic discrimination and social defeat. Journal of Trauma & Dissociation, 25 0 (3): 0 334--348, 2024. ISSN 1529-9732. doi:10.1080/15299732.2022.2120154. URL https://doi.org/10.1080/15299732.2022.2120154. doi: 10.1080/15299732.2022.2120154

work page doi:10.1080/15299732.2022.2120154 2024