Impact on Patients’ Treatment Outcomes of XpertMTB/RIF Implementation for the Diagnosis of Tuberculosis: Follow-Up of a Stepped-Wedge Randomized Clinical Trial
Bibliographic record
Abstract
INTRODUCTION: The impact on treatment outcomes of XpertMTB/RIF, a molecular-based test that provides rapid diagnosis of tuberculosis (TB) and rifampicin resistance with high accuracy, has not been reported despite its adoption in a few countries. We here report treatment outcomes in a step-wedged cluster randomized trial for patients diagnosed with XpertMTB/RIF compared to patients diagnosed with sputum smear examination in public health facilities in Brazil. METHODS: Treatment outcome data were added to the trial database of patients diagnosed from 4 February to 4 October 2012, and crosschecked with data from the national mortality and the drug-resistant TB registers. Treatment outcomes in the intervention (n=2232) and baseline (n=1856) arms were compared using a multilevel regression model. RESULTS: Unfavourable outcomes were frequent in both arms, mainly due to loss to follow-up (16%). Overall unfavourable outcomes were not reduced in the intervention arm (29.6% versus 31.7%, OR=0.93; 95%CI=0.79-1.08). However, the overall TB-attributed death rate was lower in the intervention arm (2.3% vs. 3.8%). Adjusted for HIV status, age group and city, the intervention resulted in a 35% decrease in TB-attributed deaths (OR=0.65, 95%CI=0.44-0.97). CONCLUSIONS: The proportion of patients successfully treated did not increase with Xpert MTB/RIF implementation, with high loss to follow-up rates in both arms. We did observe a 35% reduction in TB-related mortality, which we hypothesize may be explained by less advanced disease among the smear-negative patients diagnosed by Xpert. In conclusion, XpertMTB/RIF introduction did not improve TB treatment outcomes in Brazil. TRIAL REGISTRATION: clinicaltrials.gov NCT01363765.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".