<I>Mycobacterium tuberculosis</I> transmission over an 11-year period in a low-incidence, urban setting
Bibliographic record
Abstract
SETTING: Montreal, Canada, has a mean annual tuberculosis (TB) incidence of 9 per 100,000 population, 1996-2007. OBJECTIVE: To characterise potential Mycobacterium tuberculosis transmission by patient subgroups defined by age, sex, birthplace, smear and human immunodeficiency virus status, and to estimate the proportion of cases that resulted from transmission between these patient subgroups. DESIGN: Retrospective study using DNA fingerprinting techniques, with clinical and demographic information from the public health department. Among cases with matching fingerprints, a pulmonary index case was identified. The transmission index was defined as the average number of subsequent TB cases generated directly or indirectly from an index case, and was compared among subgroups, including Haitian immigrants. RESULTS: Compared to non-Haitian foreign-born index cases, Canadian-born index cases were associated with 2.38 times as many (95%CI 1.24-4.58) subsequent cases, while Haitian-born index cases were associated with 3.58 times as many (95%CI 1.74-7.36). Smear-positive index cases were not independently associated with increased transmission. However, middle-aged Canadian-born index patients were associated with a disproportionate number of subsequent cases. CONCLUSION: In Montreal, index patients from several high-risk groups are associated with subsequent transmission. This approach can be applied to other low-incidence settings to identify where targeted interventions could potentially further reduce 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".