Mobile phone text messages for improving adherence to antiretroviral therapy (ART): a protocol for an individual patient data meta-analysis of randomised trials
Bibliographic record
Abstract
INTRODUCTION: Mobile phone text messaging is emerging as an important tool in the care of people living with HIV; however, reports diverge on its efficacy in improving adherence to antiretroviral therapy (ART), and little is known about which patient groups may benefit most from phone-based adherence interventions. We will conduct an individual patient data meta-analysis to investigate the overall and subgroup effects of text messaging in three recently published text-messaging randomised controlled trials. METHODS AND ANALYSIS: Data from two Kenyan and one Cameroonian trial will be verified, reformatted and merged. We will determine pooled effect sizes for text messaging versus standard care for improving adherence to ART using individual patient random-effects meta-analysis. We will test for the interaction effects of age, gender, level of education and duration on ART. Sensitivity analyses will be conducted with regard to thresholds for adherence, methods of handling missing data and fixed-effects meta-analysis. Only anonymised data will be collected from the individual studies. ETHICS AND DISSEMINATION: Ethical approval was obtained for the individual studies. The results of this paper will be disseminated as peer-reviewed publications, at conferences and as part of a doctoral thesis. This individual patient data meta-analysis may provide important insights into the effects of text messaging on ART adherence in different subpopulations, with important implications for programme implementation involving such interventions and future research.
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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 | 0.096 | 0.142 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
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".