Virological measures and factors associated with outcomes, and missing outcome data in HIV clinical trials: a methodological study
Bibliographic record
Abstract
BACKGROUND: To evaluate the definition of HIV virological outcomes in the literature and factors associated with outcomes and missing outcome data. METHODS: We conducted a methodological review of HIV RCTs using a search (2009-2019) of PubMed, Embase and the Cochrane Central Register of Controlled Trials.Only full-text, peer-reviewed, randomised controlled trials (RCTs) that measured virological outcomes in people living with HIV, and published in English were included.We extracted study details and outcomes. We used logistic regression to identify factors associated with a viral threshold ≤50 copies/mL and linear regression to identify factors associated with missing outcome data. RESULTS: Our search yielded 5847 articles; 180 were included. A virological outcome was the primary outcome in 73.5% of studies. 89 studies (49.4%) used virological success. The remaining used change in viral load (VL) (33 studies, 18.3%); virological failure (59 studies, 32.8%); or virological rebound (9 studies, 5.0%). 96 studies (53.3%) set the threshold at ≤50 copies/mL; and 33.1% used multiple measures.Compared with government and privately funded studies, RCTs with industry funding (adjusted OR 6.39; 95% CI 2.15 to 19.00; p<0.01) were significantly associated with higher odds of using a VL threshold of ≤50 copies/mL. Publication year, intervention type, income level and number of patients were not associated with a threshold of ≤50 copies/mL. Trials with pharmacological interventions had less missing data (β=-11.04; 95% CI -20.02 to -1.87; p=0.02). DISCUSSION: Country source of funding was associated with VL threshold choice and studies with pharmacological interventions had less missing data, which may in part explain heterogeneous virological outcomes across studies. Multiple measures of VL were not associated with missing data. The development of formal guidelines on virological outcome reporting in RCTs is needed.
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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 | 0.476 | 0.776 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".