Comparison of subjective and objective adherence measures for preexposure prophylaxis against HIV infection among serodiscordant couples in East Africa
Bibliographic record
Abstract
BACKGROUND: Preexposure prophylaxis (PrEP) efficacy is highly dependent on adherence. Yet, it is unclear which adherence measures perform best for PrEP. METHODS: We compared three types of self-reported adherence questions (rating of ability to adhere, frequency of doses taken, percentage of doses taken) and three forms of objective adherence measurement [unannounced pill counts (UPC), electronic monitoring, plasma tenofovir levels] using data from an ancillary adherence study within a clinical trial of PrEP among East African serodiscordant couples (Partners PrEP Study). Monthly measures were assessed for the first 6 months of follow-up. RESULTS: One thousand, one hundred and forty-seven participants contributed 6048 person-months of data to this analysis. Median adherence was high: self-reported rating (90%), self-reported frequency (93%), and self-reported percentage (97%); UPC (99%); and electronic monitoring (97%). Prevalence of steady-state daily dosing (SSDD; ≥40 ng/ml) was 74% in a random subset of tenofovir samples obtained from 365 participants. Discrimination of SSDD versus less than SSDD levels was poor for self-reported rating [area under the receiver-operating curve (AROC) 0.54], self-reported frequency (AROC 0.52), self-reported percentage (AROC 0.56) and UPC (AROC 0.58), but moderate for electronic monitoring (AROC 0.70). Correlation was moderate among self-reported measures, adherence (0.61-0.66), but low for these self-reported measures compared with UPC (0.32-0.36) and with electronic monitoring (0.22-0.28). CONCLUSION: Electronic monitoring was the only adherence measure with meaningful ability to discriminate between SSDD and less than SSDD plasma tenofovir levels. Correlation between subjective and objective measures was poor. Future research should explore novel approaches to adherence measurement as PrEP moves into demonstration projects and programmatic implementation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".