A population-based study of risk factors for drug-resistant TB in British Columbia.
Bibliographic record
Abstract
SETTING: Provincial tuberculosis (TB) services, British Columbia, Canada. OBJECTIVE: To investigate risk factors associated with resistance to anti-tuberculosis drugs in British Columbia and to determine if there are differences in risk factor characteristics among different resistance categories. DESIGN: Using population-based data from provincial TB services, all patients with positive culture for Mycobacterium tuberculosis from 1990 to 2001 were identified and included in the study. Logistic regression analyses were performed to assess risk factors for drug resistance. RESULTS: Among 3041 eligible TB cases, 295 (10%) were found to be drug-resistant. Significant risk factors for resistance were younger age, foreign birth, ethnicity, reactivated TB and place of initial diagnosis. Foreign-born subjects (OR 3.18, 95%CI 2.26-4.49) were three times more likely to present with resistance than Canadian-born subjects. Among ethnic groups, Chinese (OR 2.32, 95%CI 1.51-3.57), South-East Asian (OR 2.92, 95%CI 1.88-4.52) and Other Asian subjects (OR 4.40, 95%CI 2.77-7.01) were 2-4 times more likely to present with resistance than Caucasians. Reactivated cases (OR 2.69, 95%CI 1.91-3.77) were three times as likely to have resistance as new cases. CONCLUSION: These results document and quantify the risk of drug-resistant disease in a large population-based cohort, and highlight patient groups who should be identified as at risk for drug-resistant disease in the industrialised world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".