Xpert MTB/RIF <sup>®</sup> cycle threshold as a marker of TB disease severity; Implications for TB treatment stratification
Bibliographic record
Abstract
Abstract Introduction Recent trials have demonstrated that shortened four-month treatment durations are effective for the majority of people with tuberculosis (TB). However, there is a population of TB patients who require longer treatment durations. Prospectively identifying those who require shorter versus longer treatment durations would support evaluation and implementation of optimized regimens. Methods We analysed data from the RIFASHORT TB treatment-shortening non-inferiority trial to define a TB phenotype classification. The RIFASHORT trial primary outcome was reanalysed using the protocol-defined non-inferiority criterion of eight percentage points, stratifying by those classified as having limited or extensive disease. Results Xpert MTB/RIF® semiquantitative bacterial burden in combination with TB disease involvement grading on chest X-ray achieved the strongest differentiation between relapse and non-relapse. The extensive disease TB phenotype (high semiquantitative bacterial burden and extensive TB disease on X-ray) accounted for one quarter of the RIFASHORT trial population and more than half of all post-treatment TB relapses (13/23). For the limited TB disease phenotype ( Conclusion A four-month treatment duration, with a widely available, tolerable, rifampicin-based TB treatment regimen, was non-inferior to the standard of care for three-quarters of people with TB in the RIFASHORT trial. This finding justifies definitive evaluation of disease-stratified rifampicin-based TB treatment in a phase III randomised trial. summary Xpert MTB/RIF® and chest radiography at TB treatment initiation identify a majority of patients for whom a 4-month treatment duration is non-inferior to the standard of care. These widely available measures may facilitate personalised TB treatment durations.
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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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".