Improving adherence to daily preexposure prophylaxis among MSM in Amsterdam by providing feedback via a mobile application
Bibliographic record
Abstract
OBJECTIVE: Improving adherence to preexposure prophylaxis (PrEP) by providing automated feedback on self-reported PrEP use via a mobile application (app). DESIGN: Randomized clinical trial among MSM participating in the Amsterdam PrEP demonstration project (AMPrEP). METHODS: Eligible participants were randomized 1 : 1 to the control or intervention app. Both allowed daily reporting of sexual behaviour and medication intake; the intervention app also provided visual feedback. Dried blood spots collected at 12 and 24 months yielded intracellular tenofovir diphosphate concentrations (TFV-DP). We assessed proportions of participants with poor (TFV-DP <700 fmol/punch; primary outcome), good (TFV-DP ≥700 fmol/punch) and excellent (TFV-DP ≥1250 fmol/punch; secondary outcome) adherence at both time-points, and the association with the control or intervention app. RESULTS: We randomized 229 participants, 118 to the intervention and 111 to the control arm. The primary, per-protocol, analysis included 83 participants per arm. In total, 22/166 (13%) of participants adhered poorly, 144/166 (87%) good and 66/166 (40%) excellently. App feedback did not result in a lower proportion of participants with poor adherence [control: 9 of 83 (11%); intervention: 13 of 83 (16%); P = 0.36]. App feedback did result in a larger proportion of participants with excellent adherence [control: 26/83 (31%); intervention: 40/83 (48%); P = 0.026]. CONCLUSION: In this highly adherent population, app feedback did not improve the proportion of participants with poor adherence to PrEP.Clinical Trial Number Netherlands Trial Register: NL5413.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".