Association between Directly Observed Therapy and Treatment Outcomes in Multidrug-Resistant Tuberculosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) represents a major obstacle towards successful TB control. Directly observed therapy (DOT) was recommended by WHO to improve adherence and treatment outcomes of MDR-TB patients, however, the effectiveness of DOT on treatment outcomes of MDR-TB patients was mixed in previous studies. We conducted this systematic review and meta-analysis to assess the association between DOT and treatment outcomes and to examine the impact of different DOT providers and DOT locations on successful treatment outcomes in MDR-TB patients. METHODS: We searched studies published in English between January 1970 and December 2015 in major electronic databases. Two reviewers independently screened articles and extracted information of DOT, treatment success rate and other characteristics of studies. Random effects model was used to calculate the pooled treatment success rate and 95% confidence interval (CI). Sub-group analyses were conducted to access factors associated with successful treatment outcomes. RESULTS: A total of 31 articles 7,466 participants were included. Studies reporting full DOT (67.4%, 95% CI: 61.4-72.8%) had significantly higher pooled treatment success rates than those reporting self-administration therapy (46.9%, 95% CI: 41.4-52.4%). No statistically difference was found among DOT provided by healthcare providers (65.8%, 95% CI: 55.7-74.7%), family members (72.0%, 95% CI: 31.5-93.5%) and private DOT providers (69.5%, 95% CI: 57.0-79.7%); and neither did we find significantly difference on pooled treatment success rates between patients having health facility based DOT (70.5%, 95% CI: 61.5-78.1%) and home-based DOT (68.4%, 95% CI: 51.5-81.5%). CONCLUSION: Providing DOT for a full course of treatment associated with a higher treatment success rate in MDR-TB patients.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".