Epidemiological evaluation of spatiotemporal and genotypic clustering of <I>Mycobacterium tuberculosis</I> in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In Canada, tuberculosis (TB) rates are at a historic low, with the remaining risk concentrated in a few vulnerable population subgroups. OBJECTIVES: To describe the epidemiology of TB in the Canadian province of Ontario and to characterise risk factors associated with transmission events, identified using genetic typing techniques. DESIGN: Retrospective analysis of 2186 culture-positive TB cases between August 2007 and December 2011. Temporal trends and risk of spatiotemporal and genotypic clustering were evaluated using Poisson and logistic regression models. RESULTS: Being in a spatiotemporal cluster was associated with Aboriginal status (odds ratio [OR] 3.63, 95% confidence interval [CI] 1.23-10.71). Cases in genotypic clusters were more likely to report homelessness as a risk factor (adjusted OR [aOR] 2.92, 95%CI 1.74-4.90) or be male (aOR 1.35, 95%CI 1.09-1.68), and were less likely to be aged ≥ 65 years (aOR 0.63, 95%CI 0.49-0.82), foreign-born (aOR 0.32, 95%CI 0.24-0.43) or Aboriginal (aOR 0.40, 95%CI 0.16-0.99). The Beijing lineage had an annual rate of increase of almost 10% (P = 0.047), and was associated with genotypic clustering (aOR 2.84, 95%CI 2.19-3.67). CONCLUSION: Genotypic data suggest that disease clusters are smaller, but far more common, than would be estimated using spatiotemporal clustering.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".