Personalized Risk Assessment of Drug-Related Harm Is Associated with Health Outcomes
Bibliographic record
Abstract
BACKGROUND: The Independent Scientific Committee on Drugs (ISCD) assigned quantitative scores for harm to 20 drugs. We hypothesized that a personalized, ISCD-based Composite Harm Score (CHS) would be associated with poor health outcomes in polysubstance users. METHODS: A prospective community sample (n=293) of adults living in marginal housing was assessed for substance use. The CHS was calculated based on the ISCD index, and the personal substance use characteristics over four weeks. Regression models estimated the association between CHS and physical, psychological, and social health outcomes. RESULTS: Polysubstance use was pervasive (95.8%), as was multimorbid illness (median 3, possible range 0-12). The median CHS was 2845 (interquartile range 1865-3977). Adjusting for age and sex, every 1000-unit CHS increase was associated with greater mortality (odds ratio [OR] 1.47, 95% confidence interval [CI] 1.07-2.01, p = 0.02), and persistent hepatitis C infection (OR 1.29, 95% CI 1.02-1.67, p = 0.04). The likelihood of substance-induced psychosis increased 1.39-fold (95% CI 1.13-1.67, p = 0.001). The amount spent on drugs increased 1.51-fold (1.40-1.62, p < 0.001) and the odds of having committed a crime increased 1.74-fold (1.46-2.10, p < 0.001). Multimorbid illness increased 1.43-fold (95% CI 1.26-1.63, p < 0.001). CONCLUSIONS: Greater CHS predicts poorer physical, psychological, and social health, and may be a useful quantitative, personalized measure of risk for drug-related harm.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".