Antiretroviral effects on HIV‐1 RNA, CD4 cell count and progression to AIDS or death: a meta‐regression analysis
Bibliographic record
Abstract
OBJECTIVE: Governments, clinicians and drug-licensing bodies have adopted changes in CD4 cell counts and HIV-1 RNA levels as evidence of effectiveness for new therapeutic interventions. We aimed to determine the strength of the association between the magnitude of the effect of changes in CD4 cell count and HIV-1 RNA and progression to AIDS or death in the highly active antiretroviral therapy (HAART) era. METHODS: We identified all randomized clinical trials (RCTs) evaluating the effect of HAART on both clinical and surrogate endpoints (1994 to September 2006). We performed a meta-regression and weighted linear regression. We additionally estimated potential RCT sample sizes that would be required to assess the effectiveness of new interventions in terms of clinical endpoints. RESULTS: We included data from 178 RCTs. We were unable to demonstrate a strong relationship at any time-point. Specifically, this was the case when CD4 T-cell change and clinical outcomes were examined at week 24 [coefficient -0.01, 95% confidence interval (CI) -0.03 to 0.001, P=0.54], week 48 (coefficient -0.01, 95% CI -0.02 to 0.001, P=0.83) and week 96 (coefficient 0.00, 95% CI -0.03 to 0.04, P=0.76). This was also the case when viral load was examined as a surrogate marker. Given the small number of clinical events occurring in new interventional RCTs, any RCT aiming to evaluate clinical endpoints within these time-points would require an exceptionally large sample size. CONCLUSIONS: Our findings indicate that, within short-term clinical trial settings, it is not possible to estimate the proportion of treatment effect associated with surrogate endpoints.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.068 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".