Effectiveness, Acceptability and Challenges of Medication Event Reminder Monitors in Tuberculosis Care: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction: Poor adherence to tuberculosis (TB) treatment, both drug-susceptible and drug-resistant, contributes to unfavourable outcomes and drug resistance. Digital adherence technologies (DATs), including medication event reminder monitors (MERM), have emerged as promising tools to support adherence. Materials and Methods: A systematic review was conducted to assess the impact of MERM on medication adherence, clinical outcomes and satisfaction amongst TB patients and healthcare providers. Studies published up to 16 February 2023 were screened across PubMed, ScienceDirect, DOAJ, Embase and Web of Science. Eligible studies included clinical trials, observational and qualitative designs. Risk of bias was assessed using the Cochrane tool for randomised controlled trials and the Newcastle–Ottawa Scale for observational studies. Results: Eight studies were included (3 trials, 4 prospective and 1 cross-sectional) with 76,811 participants. MERM was found to improve adherence and treatment outcomes. Influencing factors included age, gender, human immunodeficiency virus status, diagnosis type and patient setting. Conclusion: MERM appears effective and acceptable in TB care, although patient experiences vary. Additional research is needed to optimise DATs and tailor strategies for high-burden settings.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".