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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".