Resistance to fluoroquinolones and second-line injectable drugs: impact on multidrug-resistant TB outcomes
Bibliographic record
Abstract
A meta-analysis for response to treatment was undertaken using individual data of multidrug-resistant tuberculosis (MDR-TB) (resistance to isoniazid and rifampicin) patients from 26 centres. The analysis assessed the impact of additional resistance to fluoroquinolones and/or second-line injectable drugs on treatment outcome. Compared with treatment failure, relapse and death, treatment success was higher in MDR-TB patients infected with strains without additional resistance (n=4763; 64%, 95% CI 57-72%) or with resistance to second-line injectable drugs only (n=1130; 56%, 95% CI 45-66%), than in those having resistance to fluoroquinolones alone (n=426; 48%, 95% CI 36-60%) or to fluoroquinolones plus second-line injectable drugs (extensively drug resistant (XDR)-TB) (n=405; 40%, 95% CI 27-53%). In XDR-TB patients, treatment success was highest if at least six drugs were used in the intensive phase (adjusted OR 4.9, 95% CI 1.4-16.6; reference fewer than three drugs) and four in the continuation phase (OR 6.1, 95% CI 1.4-26.3). The odds of success in XDR-TB patients was maximised when the intensive phase reached 6.6-9.0 months duration and the total duration of treatment 20.1-25.0 months. In XDR-TB patients, regimens containing more drugs than those recommended in MDR-TB but given for a similar duration were associated with the highest odds of success. All data were from observational studies and methodologies varied between centres, therefore, the bias may be substantial. Better quality evidence is needed to optimise regimens.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.064 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".