Validation of Self-reported Opioid Agonist Treatment Among People Who Inject Drugs Using Prescription Dispensation Records
Bibliographic record
Abstract
BACKGROUND: Studies of people who inject drugs (PWID) commonly use questionnaires to determine whether participants are currently, or have recently been, on opioid agonist treatment for opioid use disorder. However, these previously unvalidated self-reported treatment measures may be susceptible to inaccurate reporting. METHODS: We linked baseline questionnaire data from 521 PWID in the Ontario integrated Supervised Injection Services cohort in Toronto (November 2018-March 2020) with record-level health administrative data. We assessed the validity (sensitivity, specificity, positive and negative predictive value [PPV and NPV]) of self-reported recent (in the past 6 months) and current (as of interview) opioid agonist treatment with methadone or buprenorphine-naloxone relative to prescription dispensation records from a provincial narcotics monitoring system, considered the reference standard. RESULTS: For self-reported recent opioid agonist treatment, sensitivity was 78% (95% CI = 72, 83), specificity was 90% (95% CI = 86, 94), PPV was 90% (95% CI = 85, 93), and NPV was 79% (95% CI = 74, 84). For self-reported current opioid agonist treatment, sensitivity was 84% (95% CI = 78, 90), specificity was 87% (95% CI = 83, 91), PPV was 74% (95% CI = 67, 81), and NPV was 93% (95% CI = 89, 95). CONCLUSIONS: Self-reported opioid agonist treatment measures were fairly accurate among PWID, with some exceptions. Inaccurate recall due to a lengthy lookback window may explain underreporting of recent treatment, whereas social desirability bias may have led to overreporting of current treatment. These validation data could be used in future studies of PWID to adjust for misclassification in similar self-reported treatment measures.
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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 | 0.001 | 0.001 |
| 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".