Effect of Intermittency on Treatment Outcomes in Pulmonary Tuberculosis: An Updated Systematic Review and Metaanalysis
Bibliographic record
Abstract
Background: Intermittent regimens offer operational advantages in tuberculosis treatment, but their efficacy has been questioned. We updated a systematic review and metaanalysis to examine the efficacy of different intermittent dosing schedules in first-line pulmonary tuberculosis therapy. Methods: An updated search included randomized control trials (RCTs) that reported on first-line pulmonary tuberculosis therapy between June 2008 and March 2016. We pooled proportions of failure, relapse, and acquired drug resistance (ADR) for 4 dosing schedules: daily throughout, thrice weekly throughout, daily (intensive phase) then thrice weekly (continuation phase), and daily (intensive phase) then twice weekly (continuation phase). Metaregression was performed using a negative binomial regression model. Results: After screening 5874 citations, 7 RCTs with 10 arms were added for a total of 56 RCTs with 110 arms. The pooled proportion of relapse was significantly higher in arms with thrice weekly therapy throughout (6.8; 95% confidence interval [CI], 3.8-9.9) and twice weekly therapy in the continuation phase (7.3; 95% CI, 3.5-11.1) when compared with daily therapy (2.5; 95% CI, 1.8-3.2; P < .01). Metaregression revealed higher rates of relapse (2.2; 95% CI, 1.2-4.0), failure (3.7; 95% CI, 1.1-12.6), and ADR (10.0; 95% CI, 2.1-46.7) in arms with thrice weekly throughout and higher rates of failure (3.0; 95% CI, 1.0-8.8) with twice weekly in the continuation phase when compared with daily therapy. Conclusion: Thrice weekly dosing throughout therapy, and twice weekly dosing in the continuation phase appear to have worse microbiological treatment outcomes when compared with daily therapy.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".