Comparison of the predictive performance of adherence measures for virologic failure detection in people living with HIV: a systematic review and pairwise meta-analysis
Bibliographic record
Abstract
A critical feature of an adherence assessment tool is its ability to predict virologic failure in people living with HIV (PLHIV). We, therefore, aimed to compare the predictive performance of commonly used adherence measures. We systematically searched MEDLINE, Embase and LILACS up to February 2018, to identify relevant observational studies comparing the effects of any two of the following adherence measurements on virologic outcomes: electronic monitoring, pill count, pharmacy refill, self-report and physician assessment. We analyzed data by pairwise meta-analyzes with a random-effects model. The proportion of virologic failures among non-adherent participants in each adherence measure was used to calculate the odds ratio (OR), with 95% Confidence Intervals (95%CI). Heterogeneity was assessed, with potential causes identified by sensitivity and subgroup analysis. We included 38 studies with individual patient data for 18,010 patients. All possible comparisons between pairs of the five adherence measures were considered and a total of nine comparison groups could be established. Meta-analysis suggested that self-report was a better predictor of virologic failure than pill count when the recall period was within one week (OR: 2.35, 95%CI: 1.07-5.18, p = 0.03). Physician assessment had higher odds of predicting virologic failure than did either self-report (OR: 2.63, 95%CI: 1.37-5.26, p < 0.01) or pharmacy refill (OR: 3.57, 95%CI: 1.69-7.14, p < 0.001). There was no difference in the predictive performance between any of the other measures that we were able to compare (p > 0.05). The combination of multiple measures did not increase the predictive value when compared to any of the measures alone. Low-cost and simple adherence measures such as self-report predict virologic failure better than or equally well as objective measures. Our results suggest that there is no need to use expensive or time-consuming adherence measures when the objective is to identify PLHIV at risk of treatment failure.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.067 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".