Prediction of clinical outcomes of human immunodeficiency virus infection in the era of highly active anti-retroviral therapy : use of repeated measures of HIV viral load and CD4 cell count
Bibliographic record
Abstract
Objective. To compare the prognostic ability of first available measurements of CD4 cell count and viral load with that of the most recent measurements and to assess the additional prognostic ability of the values of past measurements of these markers. Methods. Demographic and clinical information on 965 HIV-1 infected adults followed at a university-based HIV clinic in Montreal, Quebec were extracted from a clinical database. The prognostic ability of initial and most recent CD4 cell count and viral load measurements were assessed in a series of Cox models. The added prognostic ability of past values of measurements of these markers was explored by calculating; (i) the unweighted mean values of all previous measurements and (ii) a time-weighted mean. The differences between these mean levels and the most recent values were included as time-dependent covariates in Cox models adjusted for the value of the most recent measurements. Conclusion. The most recent measurements of CD4 cell count and viral load are more powerful predictors of clinical disease progression than initial measurements. (Abstract shortened by UMI.)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".