Prediction of clinical benefits of ritonavir‐boosted TMC114 from treatment effects on CD4 counts and HIV RNA
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to predict reductions in progression to AIDS/death associated with the treatment benefit of antiretrovirals on CD4 counts and HIV RNA in the era of highly active antiretroviral therapy (HAART). DESIGN: The study design was a pooled analysis of two trials (POWER 1 and POWER 2) of optimized background treatment plus either TMC114/ritonavir (TMC114/r) or control protease inhibitor (CPI). METHODS: Across the two randomized trials (mean baseline CD4 count 114 cells/microL and HIV RNA 4.6 log(10) HIV-1 RNA copies/mL), CD4 counts rose by a mean of 98 cells/microL for TMC114/r 600/100 mg twice a day (bid) vs. 17 cells/microL for CPI at week 24; HIV RNA fell by a median of 1.90 and 0.49 log(10) copies/mL in the two groups, respectively. For the CD4 categorization method, cohort data on rates of progression to AIDS/death during HAART within preset CD4 ranges were used to predict rates of progression during TMC114/r and CPI treatment. For the regression method, data from clinical endpoint trials were used to correlate historical treatment effects on HIV RNA and CD4 with clinical benefits. RESULTS: The CD4 categorization method predicted a 48% reduction in clinical progression to AIDS/death for TMC114/r vs. CPI. The regression method predicted a 55% reduction [95% confidence interval (CI) 45-66%] in the hazard of progression to AIDS/death based on CD4 counts, with a 47% reduction (95% CI 38-53%) predicted from effects on HIV RNA. CONCLUSIONS: Independent methods generated similar predictions of a 47-55% reduction in progression to AIDS/death for TMC114/r vs. CPI treatment, based on the changes in CD4 counts and HIV RNA from the POWER 1 and POWER 2 trials. These methods could be used to estimate clinical benefits of other antiretrovirals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".