Sensitivity and specificity of self-reported psychiatric diagnoses amongst patients treated for opioid use disorder
Bibliographic record
Abstract
BACKGROUND: Patients with opioid use disorder (OUD) frequently present with comorbid psychiatric illnesses which have significant implications for their treatment outcomes. Notably, these are often identified by self-report. Our study examined the sensitivity and specificity of self-reported psychiatric diagnoses against a structured diagnostic interview in a cohort of patients receiving outpatient pharmacological treatment for OUD. METHODS: Using cross-sectional data from adults receiving outpatient opioid agonist treatment for OUD in clinics across Ontario, Canada, we compared participants' self-reported psychiatric diagnoses with those identified by the Mini Neuropsychiatric Interview (MINI) Version 6.0 administered at the time of study entry. Sensitivity and specificity were calculated for self-report of psychiatric diagnoses. RESULTS: Amongst a sample of 683 participants, 24% (n = 162) reported having a comorbid psychiatric disorder. Only 104 of these 162 individuals (64%) reporting a comorbidity met criteria for a psychiatric disorder as per the MINI; meanwhile, 304 (75%) participants who self-reported no psychiatric comorbidity were in fact identified to meet MINI criteria for a psychiatric disorder. The sensitivity and specificity for any self-reported psychiatric diagnoses were 25.5% (95% CI 21.3, 30.0) and 78.9% (95% CI 73.6, 83.6), respectively. CONCLUSIONS: Our findings raise questions about the utility of self-reported psychiatric comorbidity in patients with OUD, particularly in the context of low sensitivity of self-reported diagnoses. Several factors may contribute to this including remittance and relapse of some psychiatric illnesses, underdiagnosis, and the challenge of differentiating psychiatric and substance-induced disorders. These findings highlight that other methods should be considered in order to identify comorbid psychiatric disorders in patients with OUD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".