The Relation between Baseline HIV Drug Resistance and Response to Antiretroviral Therapy: Re-Analysis of Retrospective and Prospective Studies Using a Standardized Data Analysis Plan
Bibliographic record
Abstract
To assess the relation between resistance to antiretroviral drugs for treatment of HIV-1 infection and virological response to therapy, results from 12 different studies were re-analysed according to a standard data analysis plan. These studies included nine clinical trials and three observational cohorts. The primary end-point in our analyses was virological failure by week 24. Baseline factors that were investigated as predictors of virological failure were plasma HIV-1 RNA, the number and type of new antiretroviral drugs in the regimen, and viral susceptibility to the drugs in the regimen, determined by genotyping or phenotyping methods. These analyses confirmed the importance of both genotypic and phenotypic drug resistance as predictors of virological failure, whether these factors were analysed separately or adjusted for other baseline confounding factors. In most of the re-analysed studies, the odds of virological failure were reduced by about twofold for each additional drug in the regimen to which the patient's virus was sensitive by genotyping methods, and by about two- to threefold for each additional drug that was sensitive by phenotyping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.236 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".