Predictors of clustering of tuberculosis in Greater Vancouver: a molecular epidemiologic study.
Bibliographic record
Abstract
BACKGROUND: The understanding of how tuberculosis is transmitted can be improved by combining DNA fingerprinting of Mycobacterium tuberculosis with conventional epidemiologic methods. We used such techniques to determine the predictors of clustering of identical isolates from tuberculosis patients in Vancouver. METHODS: We used the restriction fragment length polymorphism (RFLP) technique and, if necessary, spoligotyping to determine DNA patterns of M. tuberculosis isolates from all patients with newly diagnosed tuberculosis in Greater Vancouver reported to the Division of Tuberculosis Control from January 1995 to March 1999. Isolates were considered to be part of a cluster if they had an identical DNA pattern. We also collected demographic and epidemiologic data. Predictors associated with being in a cluster were analyzed in a multivariate logistic regression model. RESULTS: Isolates from 793 patients (430 men) were identified; 137 (17.3%) were considered to be in clusters. After adjustment for multiple potential predictors, we found that the following patients were more likely to be part of a cluster: Canadian-born Aboriginals (v. foreign-born patients) (adjusted odds ratio [OR] 6.0, 95% confidence interval [CI] 3.0-11.7), Canadian-born non-Aboriginals (v. foreign-born patients) (adjusted OR 3.6, 95% CI 2.1-6.3), and injection drug users (v. patients who did not inject drugs) (adjusted OR 3.9, 95% CI 1.9-8.1). Patients with a prior history of tuberculosis were less likely to be part of a cluster than were patients with no history of tuberculosis (adjusted OR 0.3, 95% CI 0.1-0.8). INTERPRETATION: Our findings indicate the need to target groups at high risk of tuberculosis more aggressively to prevent transmission and to treat latent infection. DNA fingerprinting may be a useful adjunct to conventional epidemiologic methods to monitor the transmission of tuberculosis in an inner-city setting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".