Consecutive rebounds in plasma viral load are associated with virological failure at 52 weeks among HIV-infected patients
Bibliographic record
Abstract
OBJECTIVES: To describe the characteristics and predictors of transient plasma viral load (pVL) rebounds among patients on stable antiretroviral therapy and to determine the effect of one or more pVL rebounds on virological response at week 52. METHODS: Individual data were combined from 358 patients from the INCAS, AVANTI-2 and AVANTI-3 studies. Logistic regression models were used to determine the relationship between the magnitude of an increase in pVL and the probability of returning to the lower limit of quantification (LLOQ: 20-50 copies/ml) and to determine the odds of virological success at 52 weeks associated with single and consecutive pVL rebounds. RESULTS: A group of 165 patients achieved a pVL nadir < LLOQ; of these, 85 patients experienced pVL rebounds within 52 weeks. The probability of a pVL rebound was greater among patients who did not adhere to treatment (68% vs 49%; P < 0.05). The probability of reachieving virological suppression after a pVL rebound was not associated with the magnitude of the rebound [odds ratio (OR), 0.86; P = 0.56] but was associated with triple therapy (OR, 2.22; P = 0.06) or non-adherence (OR, 0.40; P = 0.04). The probability of virological success at week 52 was not associated with an isolated pVL rebound but was less likely after detectable pVL at two consecutive visits. CONCLUSIONS: An isolated pVL rebound was not associated with virological success at 52 weeks but rebounds at two consecutive visits decreased the probability of later virological success. Given their high risk of short-term virological failure, patients who present with consecutive detectable pVL measurements following complete suppression should be considered ideal candidates for intervention studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".