Mobile phone short message service for adherence support and care of patients with tuberculosis infection: Evidence and opportunity
Bibliographic record
Abstract
To attain the Global End Tuberculosis (TB) goals, the treatment of persons with TB requires advancements in coordinated approaches that are low-cost and highly accessible. Treating TB successfully requires prolonged medication regimens with good adherence, which in turn requires patients to be adequately supported. Furthermore, TB care-providers often wish to monitor treatment-taking by patients in order to track the success of their programs and ensure adequate completion of therapies by individuals. The standard-of-care for treatment monitoring in TB programs often includes directly observed therapy (DOT). Video observed therapy (VOT) has emerged as a method to mimic in-person visits or observations, especially in the smartphone era with internet data connections, but remains simply inaccessible to patients in areas where TB is most endemic. Both approaches may be considered more intensive than necessary for many patients, leaving an opportunity for more affordable and acceptable approaches. The rapid increase in mobile phone penetration provides an opportunity to reach patients between clinical visits. Short message services (SMS) are available on almost every mobile phone and are supported by first generation cellular communication networks, thus providing the farthest reach and penetration globally. Evidence from non-TB conditions suggests SMS, used in a variety of ways, may support outpatients for better medication adherence and quality of care but the evidence in TB remains limited. In this paper, we discuss how basic mobile phones and SMS-related services may be used in supporting global care of persons with TB, with a focus on patient-centered approaches.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".