Predicting fatal drug poisoning (overdose) among people living with HIV-HCV co-infection
Bibliographic record
Abstract
Background: Drug poisoning (overdose) is an important public health crisis, particularly among people living with HIV and hepatitis C (HIV-HCV) co-infection.Direct-acting antivirals result in high HCV cure rates, successfully reducing liver-related mortality.However, increased rates of drug poisoning deaths will negate these benefits.Investigating the potential predictors for drug poisoning could help to reduce mortality by identifying groups most at-risk.Objective: The objective of this thesis was to predict six-month drug poisoning mortality among people with HIV-HCV coinfection using socioeconomic, behavioural, and clinical factors, including factors that are routinely measured in clinical practice, as well as those recorded for research purposes.Methods: Data from the Canadian Co-infection Cohort (CCC) were used.Participants were followed up at six-month intervals when they completed questionnaires on socio-demographic, behavioural and clinical factors.Participants were eligible for analysis if they ever reported injection or non-injection drug use between 2003 and 2023.The outcome was death due to drug poisoning within six months of a participant's cohort visit.We selected a total of 40 predictors.We used a supervised machine learning model, random forest, to develop a classification algorithm.Due to imbalanced data, we used a stratified random forest approach with undersampling.Predictors of drug poisoning were ranked in order of importance and odds ratios (OR) and 95% confidence intervals (CIs) were generated using a generalized estimating equation (GEE) regression with the top five important predictors.Four sensitivity analyses were conducted.Results: Of 2,132 total CCC participants, 1,998 met the eligibility criteria for this analysis.Of those eligible, 1,764 (88.3%) reported ever using injection drugs and 1,807 (90.4%) reported LIST OF ABBREVIATIONSHIV: Human immunodeficiency virus AIDS: Acquired immunodeficiency syndrome ART: Antiretroviral therapy STBBI: Sexually transmitted blood borne infections PWID: People who inject drugs HCV: Hepatitis C virus DAA: Direct-acting antiviral CCC: Canadian Co-infection Cohort gbMSM: Gay, bisexual, and other men-who-have-sex-with-men PWUD: People who use drugs POC: Point-of-care MSM: Men who have sex with men U=U: Undetectable = Untransmissible PrEP: Pre-exposure prophylaxis PEP: Post-exposure prophylaxis
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".