Time-Dependence of Survival Predictions Based on Markers of HIV Disease
Bibliographic record
Abstract
To determine whether the ability of baseline clinical stage, viremia, and CD4 cell counts to predict mortality in HIV-1-infected patients changes with duration of follow-up. Three hundred ninety-four patients were followed for an average of 29 months by the Swiss HIV Cohort Study, a practice-based registry of HIV-1-infected patients in Switzerland. Predictor variables were the baseline clinical stage, CD4 cell count, circulating HIV-1 RNA level, and the RNA/CD4 ratio; the outcome was death. The changes in relative risks of death over time were examined using survival models that extend the Cox model to allow for nonproportionality of hazards. During 949 person-years of follow-up, 169 patients died (mortality rate 17.8 per 100 person-years). Compared with clinical stage A, patients in stages B and C at baseline had much higher mortality rates in the subsequent year. The prognostic ability of stage C decayed over time (P=0.03). By contrast, the relative risks associated with a 2-fold difference in CD4 counts remained remarkably stable, at ~0.6 (P=0.81 for the time-dependence test). Relative hazards associated with a 10-fold difference in HIV RNA per milliliter and in HIV RNA per CD4 cell both tended to increase over time, but this trend failed to reach statistical significance (P=0.21 and P=0.08, respectively). Time-dependence patterns of prognostic ability varied widely among predictors, displaying gradual decay (clinical stage), stability (CD4 cells), and a trend to progressive increase (viremia). These results may affect clinical monitoring of HIV-infected patients and the interpretation of cohort studies of HIV-infected patients.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".