Study protocol and implementation details for a pragmatic, stepped-wedge cluster randomised trial of a digital adherence technology to facilitate tuberculosis treatment completion
Bibliographic record
Abstract
INTRODUCTION: Low-cost digital adherence technologies (DATs) such as 99DOTS have emerged as an alternative to directly observed therapy (DOT), the current standard for tuberculosis (TB) treatment supervision. However, there are limited data to support DAT scale-up. The 'DOT to DAT' trial aims to evaluate the effectiveness and implementation of a 99DOTS-based TB treatment supervision strategy. METHODS AND ANALYSIS: This is a pragmatic, stepped-wedge cluster randomised trial, with hybrid type 2 effectiveness-implementation design. The trial will include all adults (estimated N=1890) treated for drug-susceptible pulmonary TB over an 8-month period at 18 TB treatment units in Uganda. Three sites per month will switch from routine care (DOT) to the intervention (99DOTS-based treatment supervision) beginning in month 2, with the order determined randomly. 99DOTS enables patients to be monitored while self-administering TB medicines. Patients receive daily automated short message service (SMS) dosing reminders and confirm dosing by calling toll-free numbers. The primary effectiveness outcome is the proportion of patients completing TB treatment. With 18 clusters randomised into six steps and an average cluster size of 15 patients per month, the study will have 89% power to detect a 10% or greater increase in treatment completion between the routine care and intervention periods. Secondary outcomes include more proximal effectiveness measures as well as quantitative and qualitative assessments of the reach, adoption and implementation of the intervention. ETHICS AND DISSEMINATION: Ethics approval was granted by institutional review boards at Makerere University School of Public Health and the University of California San Francisco. Findings will be disseminated through peer-reviewed publications, presentations at scientific conferences and presentations to key stakeholders. TRIAL REGISTRATION NUMBER: PACTR201808609844917.
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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 | 0.060 | 0.064 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.259 | 0.038 |
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".