Effect of Mycobacterial Drug Resistance Patterns on Patients’ Survival: A Cohort Study in Thailand
Bibliographic record
Abstract
BACKGROUND: Drug resistance substantially increases tuberculosis (TB) mortality. This study aimed to describe the prevalence of mycobacterial drug resistance pattern and association of common resistance patterns with TB mortality in Thailand. METHOD: A retrospective cohort study was conducted using TB surveillance data. A total of 9,518 culture-confirmed, pulmonary TB patients registered from 1 October 2004 to 31 December 2008 from the Thailand TB Active Surveillance Network were included in this study. Patients were followed up until TB treatment completion or death. Mycobacterial drug resistance patterns were categorized as pan-susceptible, rifampicin resistance, isoniazid monoresistance, and ethambutol/streptomycin resistance. Drug susceptibility testing (DST) was determined by Mycobacterial Growth Indicator Tube (MGIT) liquid culture systems. Survival analysis was applied. RESULT: Isoniazid monoresistance was the most common pattern, while rifampicin resistance had the largest impact on mortality. Cox regression analysis showed a significantly higher risk of death among patients with rifampicin resistance (adjusted hazard ratio (aHR) 1.9, 95% confident interval (CI), 1.5-2.5) and isoniazid monoresistance (aHR 1.4, 95% CI 1.1-1.7) than those with pan-susceptible group after adjustment for age, nationality, human immunodeficiency virus (HIV) and antiretroviral therapy (ART) status, diabetes mellitus, cavitary disease on chest x-ray, treatment observation, and province. HIV co-infection was associated with higher mortality in patients both on ART (aHR 1.9, 95% CI 1.5-2.5) and not on ART (aHR 8.1, 95% CI 6.8-9.8). CONCLUSION: Rifampicin resistance and isoniazid monoresistance were associated with increased TB mortality. HIV-coinfection was associated with a higher risk of death including among those taking antiretroviral therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".