Interventions to improve retention-in-care and treatment adherence among patients with drug-resistant tuberculosis: a systematic review
Bibliographic record
Abstract
The global loss to follow-up (LTFU) rate among drug-resistant tuberculosis (DR-TB) patients remains high at 15%. We conducted a systematic review to explore interventions to reduce LTFU during DR-TB treatment. We searched for studies published between January 2000 and December 2017 that provided any form of psychosocial or material support for patients with DR-TB. We estimated point estimates and 95% confidence intervals of the proportion LTFU. We performed subgroup analyses and pooled estimates using an exact binomial likelihood approach. We included 35 DR-TB cohorts from 25 studies, with a pooled proportion LTFU of 17 (12–23)%. Cohorts that received any form of psychosocial or material support had lower LTFU rates than those that received standard care. Psychosocial support throughout treatment,viacounselling sessions or home visits, was associated with lower LTFU rates compared to when support was provided through a limited number of visits or not at all. Our review suggests that psychosocial support should be provided throughout DR-TB treatment in order to reduce treatment LTFU. Future studies should explore the potential of providing self-administered therapy complemented with psychosocial support during the continuation phase.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".