Impact of pyrazinamide resistance on multidrug-resistant tuberculosis in Karakalpakstan, Uzbekistan
Bibliographic record
Abstract
SETTING: The World Health Organization (WHO) recommends the inclusion of pyrazinamide (PZA) in treatment regimens for multidrug-resistant tuberculosis (MDR-TB) unless resistance has been confirmed. OBJECTIVE: To investigate the association between PZA susceptibility and MDR-TB treatment outcome among patients treated with a PZA-containing regimen and whether the duration of the intensive phase of the PZA-containing regimen affected treatment outcome. DESIGN: We conducted a retrospective cohort study including all eligible MDR-TB patients starting treatment in 2003-2013 in the TB programme in Karakalpakstan, Uzbekistan. PZA drug susceptibility testing (DST) using liquid culture was performed, and outcomes were classified according to the WHO 2013 definitions. RESULTS: Of 2446 MDR-TB patients included, 832 (34.0%) had an available baseline PZA DST result, 612 (73.6%) of whom were PZA-resistant. We found no association between treatment success and PZA susceptibility (adjusted odds ratio [aOR] 0.86, 95%CI 0.51-1.44, P = 0.6) in patients treated with PZA. Furthermore, among patients with no baseline PZA DST result, no evidence was seen of an association between treatment success and PZA treatment duration (aOR 0.86, 95%CI 0.49-1.51, P = 0.6). CONCLUSION: Treatment of MDR-TB with a standard PZA regimen does not appear to improve treatment outcomes, regardless of PZA susceptibility or duration of treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".