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 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 | 0.063 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".