Transmission of tuberculosis from smear negative patients: a molecular epidemiology study
Bibliographic record
Abstract
BACKGROUND: While smear positive patients with tuberculosis (TB) are considered more infectious than smear negative patients, the latter can also transmit TB. METHODS: In a molecular epidemiology study of 791 patients in the Greater Vancouver regional district, the number of episodes of TB transmission from two groups of smear negative clustered patients by RFLP (assumed to be involved in recent transmission) was estimated after assessing for potential bias. Group 1 (n = 79) included patients with pulmonary TB or pulmonary + extrapulmonary disease (PTB or PTB+EPTB); group 2 (n = 129) included all patients in group 1 + extrapulmonary cases alone. RESULTS: In the total sample the mean (SD) age was 51 (21) years, 54.3% were male, and 17.0% of patients were clustered. Compared with smear negative patients, smear positive patients were more likely to be in a cluster (OR = 2.0, 95% CI 1.1 to 3.6) and to have had a history of ethanol abuse (OR = 2.7, 95% CI 1.0 to 6.7), diabetes mellitus (OR = 2.8, 95% CI 1.1 to 7.0), injection drug use (OR = 3.1, 95% CI 1.1 to 8.3), and to have had a previous hospital admission (OR = 8.5, 95% CI 5.1 to 14.0). The proportion of episodes of transmission from smear negative clustered patients ranged from 17.3% to 22.2% in group 1 and from 25% to 41% in group 2. CONCLUSION: In Greater Vancouver, smear negative cases appear responsible for at least one sixth of culture positive episodes of TB 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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".