Risk factors for drug-resistant tuberculosis at a referral centre in Toronto, Ontario, Canada: 2010–2016
Bibliographic record
Abstract
BACKGROUND: Drug-resistant tuberculosis (TB) poses a major public health concern worldwide. However, no studies have addressed risk factors for drug resistance in Ontario, which has its own unique profile of immigrants. We evaluated demographic and clinical risk factors for drug-resistant TB among patients treated at West Park Healthcare Centre, located in Toronto, Ontario (Canada). METHODS: All patients who were diagnosed with TB and treated at West Park Healthcare Centre between January 2010 and December 2016 were included in this retrospective cohort study. Characteristics of patients with isoniazid mono-resistant (INH-R) TB and multidrug resistant (MDR) TB were compared to patients with drug-susceptible TB with bivariate and multivariable logistic regression. RESULTS: Risk factors for INH-R TB included younger age (younger than 35 years), prior TB treatment, non-diabetic and birth in a non-South-East Asian country, but only the latter two factors were significant in multivariable analysis. On the other hand, we found younger generation (younger than 65 years), birth in European region, recent arrival to Canada (fewer than 120 months), prior treatment and human immunodeficiency virus (HIV) infection were associated with MDR-TB, among which younger age (younger than 35 years), more recent immigration (fewer than 24 months), prior treatment and HIV infection were significant in multivariable analysis. CONCLUSION: These findings may be of use to TB clinicians in the province by informing the initial empiric antibiotic regimen prescribed while awaiting phenotypic drug susceptibility testing and assisting in decisions regarding whether to request rapid molecular drug susceptibility testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".