Predicting fatal drug poisoning among people living with HIV-HCV co-infection
Bibliographic record
Abstract
Background: Drug poisoning (overdose) is a public health crisis, particularly among people living with HIV and hepatitis C (HCV) co-infection. Identifying potential predictors of drug poisoning could help decrease drug-related deaths. Methods: Data from the Canadian Co-infection Cohort were used to predict death due to drug poisoning within 6 months of a cohort visit. Participants were eligible for analysis if they ever reported drug use. Supervised machine learning (stratified random forest with undersampling to account for imbalanced data) was used to develop a classification algorithm using 40 sociodemographic, behavioural, and clinical variables. Predictors were ranked in order of importance, and odds ratios and 95% confidence intervals (CIs) were generated using a generalized estimating equation regression. Results: Of 2,175 study participants, 1,998 met the eligibility criteria. There were 94 drug poisoning deaths, 53 within 6 months of a last visit. When applied to the entire sample, the model had an area under the curve (AUC) of 0.9965 (95% CI, 0.9941-0.9988). However, the false-positive rate was high, resulting in a poor positive predictive value (1.5%). Our model did not generalize well out of sample (AUC 0.6, 95% CI 0.54-0.68). The top important variables were addiction therapy (6 months), history of sexually transmitted infection, smoking (6 months), ever being on prescription opioids, and non-injection opioid use (6 months). However, no predictor was strong. Conclusions: Despite rich data, our model was not able to accurately predict drug poisoning deaths. Larger datasets and information about changing drug markets could help improve future prediction efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".