Xpert MTB/RIF Cycle Threshold as a Marker of Tuberculosis (TB) Disease Severity: Implications for TB Treatment Stratification
Bibliographic record
Abstract
BACKGROUND: Recent trials have demonstrated that shortened 4-month treatment durations are effective for the majority of people with tuberculosis (TB). However, there is a population of patients with TB 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 analyzed data from the RIFASHORT TB treatment-shortening noninferiority trial to define a TB phenotype classification. The RIFASHORT trial primary outcome was reanalyzed using the protocol-defined noninferiority criterion of 8 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 nonrelapse. 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 posttreatment TB relapses (13/23). For the limited TB disease phenotype (a semiquantitative bacterial burden other than high or no extensive TB disease on X-ray), the experimental 4-month 1200-mg rifampicin-containing regimen met the protocol-defined noninferiority criterion in both modified intention-to-treat (adjusted risk difference: -1.3%; 95% CI, -6.7% to 4.0%) and per protocol analyses (1.7%; 95% CI, -3.8% to 7.1%). CONCLUSIONS: The TB phenotype classification derived here successfully identified three-quarters of RIFASHORT trial participants for whom a 4-month 1200-mg rifampicin regimen was noninferior to the 6-month standard of care. A definitive phase III randomized trial of disease-stratified rifampicin-based TB treatment is justified.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".