Postsanatorium Pattern of Antituberculous Drug Resistance in the Canadian-born Population of Western Canada: Effect of Outpatient Care and Immigration
Bibliographic record
Abstract
Concurrent with the shift in tuberculosis case management from sanatorium to outpatient setting was a shift in the continent of origin (Europe to Asia) of most new immigrants to CANADA: To assess the impact of these two events on antituberculous drug resistance in the Canadian-born population, the authors reviewed the results of six drug resistance surveys conducted in the two westernmost provinces of Canada between 1963 and 1994. Survey data were complemented by new molecular diagnostic and contact tracing data collected over 5 years (1994--1998) in one of the three large urban centers of the region. Over the time spanned by the surveys, there was no increase in the proportion of all Canadian-born tuberculosis cases who relapsed or the proportion of all Canadian-born relapsed cases who were drug resistant (approximately 12--13%). In addition, the prevalence of primary drug resistance among Canadian-born cases did not increase; rates consistently averaged between 2% and 5% despite a doubling of primary resistance rates among foreign-born cases. Molecular diagnostic and contact tracing data strongly supported the survey findings. The authors concluded that outpatient care and immigration have thus far had no measurable impact on the pattern of antituberculous drug resistance in the Canadian-born population of western CANADA:
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".