A Prognostic Model for HIV Seroconversion Among Injection Drug Users as a Tool for Stratification in Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: The main goal of this study was to construct a prognostic model for HIV seroconversion among injection drug users (IDUs) using easy-to-measure risk indicators. DESIGN: Cox proportional hazards regression modeling was used for risk stratification in a heterogeneous population of IDUs with regards to HIV risk-taking behaviors. METHODS: Subjects were recruited in a prospective cohort of IDUs followed between September 1992 and October 2001. A total of 1602 men, seronegative at enrollment with at least 1 follow-up visit, were included in the analyses. Only variables that consistently predict HIV seroconversion in several settings were considered. The final model was used to assign a risk score for each participant. RESULTS: Three risk indicators were included in the risk score to predict HIV seroconversion: unstable housing, average cocaine injections per day, and having shared a syringe with a known HIV-positive partner. Kaplan-Meier survival functions were generated and risk score values stratified in 3 groups. HIV incidence rates per 100 person-years were as follows: 0.91 (95% CI, 0.55-1.52) for the low-risk group, 3.10 (95% CI, 2.49-3.84) for the moderate-risk group, and 7.82 (95% CI, 6.30-9.73) for the high-risk group (log-rank P value < 0.0001). CONCLUSION: If validated in other settings, this risk score may improve the prediction of outcome and allow more accurate stratification in clinical trials.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".