The impact of digital health technologies on tuberculosis treatment: a systematic review
Why this work is in the frame
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Bibliographic record
Abstract
Digital technologies are increasingly harnessed to support treatment of persons with tuberculosis (TB). Since in-person directly observed treatment (DOT) can be resource intensive and challenging to implement, these technologies may have the potential to improve adherence and clinical outcomes. We reviewed the effect of these technologies on TB treatment adherence and patient outcomes.We searched several bibliographical databases for studies reporting the effect of digital interventions, including short message service (SMS), video-observed therapy (VOT) and medication monitors (MMs), to support treatment for active TB. Only studies with a control group and which reported effect estimates were included.Four trials showed no statistically significant effect on treatment completion when SMS was added to standard care. Two observational studies of VOT reported comparable treatment completion rates when compared with in-person DOT. MMs increased the probability of cure (RR 2.3, 95% CI 1.6-3.4) in one observational study, and one trial reported a statistically significant reduction in missed treatment doses relative to standard care (adjusted means ratio 0.58, 95% CI 0.42-0.79).Evidence of the effect of digital technologies to improve TB care remains limited. More studies of better quality are needed to determine how such technologies can enhance programme performance.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it