Impact of country of origin on drug-resistant tuberculosis among foreign-born persons in British Columbia.
Bibliographic record
Abstract
SETTING: Provincial tuberculosis (TB) services, British Columbia, Canada. OBJECTIVES: To estimate the risk of drug resistance among foreign-born TB patients and to identify risk factors associated with drug resistance. DESIGN: Using the provincial TB database, we examined all culture-positive foreign-born TB patients for the years 1990-2001. The risk of having a drug-resistant isolate was estimated according to country and region of origin. RESULTS: Of 1940 foreign-born patients identified, 247 (12.7%, 95%CI 11.3-14.3) cases had isolates resistant to at least one of the first-line drugs, with 160 (8.3%) isolates showing monoresistance, 24 (1.2%) multidrug resistance (resistance to at least isoniazid and rifampin) and 63 (3.3%) polyresistance (resistance to two or more drugs, excluding MDR). Country-specific analysis showed that immigrants from Vietnam (adjusted OR 2.12, 95%CI 1.37-3.27) and the Philippines (adjusted OR 1.71, 95%CI 1.10-2.66) had a significantly higher risk of resistance than other immigrants. In addition, the risk was the highest for younger TB patients and patients with reactivated disease (adjusted OR 2.12, 95%CI 1.09-4.09). CONCLUSION: The risk of drug resistance was the highest among foreign-born patients from Vietnam and the Philippines. These findings should assist clinicians in prescribing and tailoring anti-tuberculosis regimens for immigrants more appropriately.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".