Treatment outcomes for isoniazid-resistant tuberculosis under program conditions in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Every year, over 1 million people develop isoniazid (INH) resistant tuberculosis (TB). Yet, the optimal treatment regimen remains unclear. Given increasing prevalence, the clinical efficacy of regimens used by physicians is of interest. This study aims to examine treatment outcomes of INH resistant TB patients, treated under programmatic conditions in British Columbia, Canada. METHODS: Medical charts were retrospectively reviewed for cases of culture-confirmed INH mono-resistant TB reported to the BC Centre for Disease Control (BCCDC) from 2002 to 2014. Treatment regimens, patient and strain characteristics, and clinical outcomes were analysed. RESULTS: One hundred sixty five cases of INH mono-resistant TB were included in analysis and over 30 different treatment regimens were prescribed. Median treatment duration was 10.5 months (IQR 9-12 months) and treatment was extended beyond 12 months for 26 patients (15.8%). Fifty six patients (22.6%) experienced an adverse event that resulted in a drug regimen modification. Overall, 140 patients (84.8%) had a successful treatment outcome while 12 (7.2%) had an unsuccessful treatment outcome of failure (n = 2; 1.2%), relapse (n = 4; 2.4%) or all cause mortality (n = 6; 3.6%). CONCLUSION: Our treatment outcomes, while consistent with findings reported from other studies in high resource settings, raise concerns about current recommendations for INH resistant TB treatment. Only a small proportion of patients completed the recommended treatment regimens. High quality studies to confirm the effectiveness of standardized regimens are urgently needed, with special consideration given to trials utilizing fluoroquinolones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".