Isoniazid resistance pattern among pulmonary tuberculosis patients in Bangladesh: An exploratory study
Bibliographic record
Abstract
OBJECTIVES: In high tuberculosis (TB) burden countries such as Bangladesh, research and policy tend to focus on rifampicin (RIF)-resistant TB patients, leaving RIF-sensitive but isoniazid (INH)-resistant (Hr-TB) patients undiagnosed. Our study aims to determine the prevalence of INH resistance among pulmonary TB patients in selected health care facilities in Bangladesh. METHODS: This study was conducted across nine TB Screening and Treatment Centres situated in Bangladesh. Sputum samples from 1084 Xpert-positive pulmonary TB patients were collected between April 2021 and December 2022 and cultured for drug susceptibility testing. Demographic and clinical characteristics of Hr-TB and drug-susceptible TB patients were compared. RESULTS: Among available drug susceptibility testing results of 998 culture-positive isolates, the resistance rate of any INH regardless of RIF susceptibility was 6.4% (64/998, 95% CI: 4.9-8.2). The rate was significantly higher in previously treated (21.1%, 16/76, 95% CI: 12.0-34.2) compared with newly diagnosed TB patients (5.2%, 48/922, 95% CI: 3.8-6.9) (p < 0.001). The rate of Hr-TB was 4.5% (45/998, 95% CI: 3.3-6.0), which was also higher among previously treated patients (6.6%, 5/76, 95% CI: 1.4-13.5) compared with newly diagnosed TB patients (4.3%; 40/922, 95% CI: 3.1-5.9) (p 0.350). Most importantly, the rate of Hr-TB was more than double compared with MDR-TB (4.5%, 45/998, vs. 1.9%, 19/998) found in the current study. DISCUSSION: This study reveals a high prevalence of Hr-TB, surpassing even that of the multi-drug-resistant TB in Bangladesh. This emphasizes the urgent need to adopt WHO-recommended molecular tools at the national level for rapid detection of INH resistance so that patients receive timely and appropriate 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".