Does phone messaging improves tuberculosis treatment success? A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Compliance to anti-TB treatment is crucial in achieving cure and avoiding the emergence of drug resistance. Electronic health (eHealth) interventions are included in the strategy to end the global Tuberculosis (TB) epidemic by 2035. Evidences showed that mobile messaging systems could improve patient adherence to clinic appointment for diagnosis and treatment. This review aimed to assess the effect of mobile-phone messaging on anti-TB treatment success. Methods All randomized controlled trial (RCT) and quasi-experimental studies done prior to August 26, 2019 were included in the review. Studies were retrieved from PubMed, EMBASE, Cochrane and ScienceDirect databases including, grey and non-indexed literatures from Google and Google scholar. Quality of studies were independently assessed using Cochrane Risk of Bias Assessment Tool. A qualitative synthesis and quantitative pooled estimation were used to measure the effect of phone messaging on TB treatment success rate. PRISMA flow diagrams were used to summarize article selection process. Results A total of 1237 articles were identified, with 14 meeting the eligibility criteria for qualitative synthesis. Eight studies with a total of 5680 TB patients (2733 in intervention and 2947 in control groups) were included in meta-analysis. The pooled effect of mobile-phone messaging revealed a small increase in treatment success compared to standard of care (RR 1.04, 95% CI 1.02 to 1.06), with low heterogeneity (I 2 = 7%, p < 0.0002). In the review, performance, detection and attrition biases were reported as major risk of biases. Conclusions Mobile-phone messaging showed a modest effect in improving anti-TB treatment success; however, the quality of evidence was low. Further controlled studies are needed to increase the evidence-base on the role of mHealth interventions to improve TB care. Protocol registration number CRD420170744339. http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017074439
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".