Pretreatment attrition after rifampicin-resistant tuberculosis diagnosis with Xpert MTB/RIF or ultra in high TB burden countries: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The WHO endorsed the Xpert MTB/RIF (Xpert) technique since 2011 as initial test to diagnose rifampicin-resistant tuberculosis (RR-TB). No systematic review has quantified the proportion of pretreatment attrition in RR-TB patients diagnosed with Xpert in high TB burden countries.Pretreatment attrition for RR-TB represents the gap between patients diagnosed and those who effectively started anti-TB treatment regardless of the reasons (which include pretreatment mortality (death of a diagnosed RR-TB patient before starting adequate treatment) and/or pretreatment loss to follow-up (PTLFU) (drop-out of a diagnosed RR-TB patient before initiation of anti-TB treatment). METHODS: In this systematic review and meta-analysis, we queried EMBASE, PubMed and Web of science to retrieve studies published between 2011 and 22 July 2024, that described pretreatment attrition for RR-TB using Xpert in high TB burden countries. Data on RR-TB patients who did not start treatment after diagnosis and reasons for not starting were extracted in an Excel table. A modified version of the Newcastle-Ottawa scale was used to evaluate the risk of bias among all included studies. The pooled proportion of pretreatment attrition and reasons were assessed using random-effects meta-analysis. Forest plots were generated using R software. RESULTS: Thirty eligible studies from 21 countries were identified after full-text screening and included in the meta-analysis. Most studies used routine programme data. The pooled proportion of pretreatment attrition in included studies was 18% (95% CI: 12 to 25). PTLFU and pretreatment mortality were, respectively, reported in 10 and nine studies and explained 78% (95% CI: 51% to 92%) and 30% (95% CI: 15% to 52%) of attrition. CONCLUSION: Pretreatment attrition was widespread, with significant heterogeneity between included studies. National TB programmes should ensure accurate data collection and reporting of pretreatment attrition to enable reliable overall control strategies. PROSPERO REGISTRATION NUMBER: CRD42022321509.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.053 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".