Directly Observed Therapy to Measure Adherence to Tuberculosis Medication in Observational Research: Protocol for a Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: A major challenge for prospective, clinical tuberculosis (TB) research is accurately defining a metric for measuring medication adherence. OBJECTIVE: We aimed to design a method to capture directly observed therapy (DOT) via mobile health carried out by community workers. The program was created specifically to measure TB medication adherence for a prospective TB cohort in Western Cape Province, South Africa. METHODS: Community workers collect daily adherence data on mobile smartphones. Participant-level adherence, program-level adherence, and program function are systematically monitored to assess DOT program implementation. A data dashboard allows for regular visualization of indicators. Numerous design elements aim to prevent or limit data falsification and ensure study data integrity. RESULTS: The cohort study is ongoing and data collection is in progress. Enrollment began on May 16, 2017, and as of January 12, 2021, a total of 236 participants were enrolled. Adherence data will be used to analyze the study's primary aims and to investigate adherence as a primary outcome. CONCLUSIONS: The DOT program includes a mobile health application for data collection as well as a monitoring framework and dashboard. This approach has potential to be adapted for other settings to improve the capture of medication adherence in clinical TB research. TRIAL REGISTRATION: Clinicaltrials.gov NCT02840877; https://clinicaltrials.gov/ct2/show/NCT02840877.
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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 | 0.121 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".