Predicting HIV RNA Virologic Outcome at 52-Weeks Follow-Up in Antiretroviral Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To determine the ability of intermediate plasma viral load (pVL) measurements to predict virologic outcome at 52 weeks of follow-up in clinical trials of antiretroviral therapy. METHODS: Individual patient data from three clinical trials (INCAS, AVANTI-2 and AVANTI-3) were combined into a single database. Virologic success was defined to be plasma viral load (pVL) <500 copies/ml at week 52. The sensitivity and specificity of intermediate pVL measurements below the limit of detection, 100, 500, 1000, and 5000 copies/ml to predict virologic success were calculated. RESULTS: The sensitivity, specificity, and positive and negative predictive values of a pVL measurement <1000 copies/ml at week 16 to predict virologic outcome at week 52 were 74%, 74%, 48%, and 90%, respectively, for patients on double therapy. For patients on triple therapy, the sensitivity, specificity, and positive and negative predictive values of a pVL measurement <50 copies/ml at week 16 to predict virologic outcome were 68%, 68%, 80%, and 47%, respectively. CONCLUSIONS: For patients receiving double therapy, a poor virologic result at an intermediate week of follow-up is a strong indicator of virologic failure at 52 weeks whereas intermediate virologic success is no guarantee of success at 1 year. For patients on triple therapy, disappointing intermediate results do not preclude virologic success at 1 year and intermediate successes are more likely to be sustained.
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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 | 0.062 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".