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Record W4403709366 · doi:10.2196/53510

Occurrence of Stigmatizing Documentation Among Hospital Medicine Encounters With Opioid-Related Diagnosis Codes: Cohort Study

2024· article· en· W4403709366 on OpenAlexvenueno aff
William Bradford, Reed W R Bratches, Hollie Porras, David R. Chen, Kelly Gagnon, Simon B. Ascher

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Institutes of HealthNational Center for Advancing Translational SciencesAgency for Healthcare Research and QualityAmerican Heart Association
KeywordsPolysubstance dependenceMedicineDocumentationPsychiatryMedical recordSubstance abuseLogistic regressionFamily medicinePoison controlOdds ratioMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Physician use of stigmatizing language in the clinical documentation of hospitalized adults with opioid use is common. However, patient factors associated with stigmatizing language in this setting remain poorly characterized. OBJECTIVE: This study aimed to determine whether specific demographic factors and clinical outcomes are associated with the presence of stigmatizing language by physicians in the clinical documentation of encounters with opioid-related ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes. METHODS: Hospital encounters with one or more associated opioid-related ICD-10 admission diagnoses on the hospital medicine service during the 2020 calendar year were analyzed for the presence of stigmatizing language in history and physical and discharge summaries. Multivariable adjusted logistic regression models were used to determine associations of age, race, gender, medication for addiction treatment use, against medical advice discharge, homelessness, comorbid polysubstance use, comorbid psychiatric disorder, comorbid chronic pain, cost, and 30-day readmission with the presence of stigmatizing language. RESULTS: A total of 221 encounters were identified, of which 64 (29%) encounters had stigmatizing language present in physician documentation. Most stigmatizing language was due to use of "substance abuse" rather than the preferred term "substance use" (63/66 instances). Polysubstance use and homelessness were independently associated with the presence of stigmatizing language (adjusted odds ratio [aOR] 7.83; 95% CI 3.42-19.24 and aOR 2.44; 95% CI 1.03-5.90) when controlling for chronic pain and other covariates. CONCLUSIONS: Among hospital medicine encounters with an opioid-related diagnosis, stigmatizing language by physicians in clinical documentation was common and independently associated with comorbid polysubstance use and homelessness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.398
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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