Predictors of Virological Response in HIV-Infected Patients to Salvage Antiretroviral Therapy that Includes Nelfinavir
Bibliographic record
Abstract
Different salvage strategies have been used to regain control in patients with HIV who have virological failure on combination antiretroviral therapy. We conducted a cohort study of 63 extensively antiretroviral pretreated patients who initiated nelfinavir as part of salvage therapy, to determine predictors of virological response. The maximum HIV RNA response was >0.5 log10 copies/ml reduction in 43 patients (68%), including 21 patients (33%) who had suppression to <500 copies/ml. Corresponding response rates at 24 weeks were 41 and 19%, respectively. Responders and non-responders could not be distinguished by mean baseline HIV RNA or CD4 cell count, duration of prior protease inhibitor (PI) use, introduction of an initial non-nucleoside reverse transcriptase inhibitor or the number of antiretroviral agents changed when nelfinavir was added, likely reflecting the homogeneity of the population studied. The only parameter predictive of response was virus genotype. Response rates were lower in patients with increasing numbers of primary (P=0.045) or secondary (P=0.001) PI mutations. The addition of increasing numbers of reverse transcriptase mutations further impaired response rates (P=0.004).
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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 | 0.004 |
| 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 | 0.001 | 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".