Use of geographic and genotyping tools to characterise tuberculosis transmission in Montreal.
Bibliographic record
Abstract
SETTING: In Canada, tuberculosis (TB) is increasingly an urban health problem. Montreal is Canada's second-largest city and the second most frequent destination for new immigrants and refugees. OBJECTIVES: To detect spatial aggregation of cases, areas of excess incidence and local 'hot spots' of transmission in Montreal. DESIGN: We used residential addresses to geocode active TB cases reported on the Island of Montreal in 1996-2000. After a hot spot analysis suggested two areas of overconcentration, we conducted a spatial scan, with census tracts (population 2500-8000) as the primary unit of analysis and stratification by birthplace. We linked these analyses with genotyping of all available Mycobacterium tuberculosis isolates, using IS6110-RFLP and spoligotyping. RESULTS: We identified four areas of excess incidence among the foreign-born (incidence rate ratios 1.3-4.1, relative to the entire Island) and one such area among the Canadian-born (incidence rate ratio 2.3). There was partial overlap with the two hot spots. Genotyping indicated ongoing transmission among the foreign-born within the largest high-incidence zone. While this zone overlapped the area of high incidence among Canadian-born, genotyping largely excluded transmission between the two groups. CONCLUSIONS: In a city with low overall incidence, spatial and molecular analyses highlighted ongoing local transmission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".