The use of maximum likelihood methods to estimate the risk of tuberculous infection and disease in a Canadian First Nations population
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) notification rates among First Nations people in British Columbia, Canada, are higher than those among non-First Nations people, although rates are declining more rapidly in the First Nations population. The epidemiology of tuberculous infection and disease during the period 1926-2000 in this population was investigated. METHODS: The trend in the annual risk of infection (ARI) since 1926 was estimated using tuberculous meningitis mortality statistics and skin testing data. Risks of progression from infection to disease were estimated by fitting model predictions of disease incidence to TB notifications, using maximum likelihood methods. Infectious TB notifications were matched with ARI estimates to obtain the number of transmissions per infectious case over time. RESULTS: We estimate that the ARI decreased from more than 10% during the prechemotherapy era to less than 0.1% by 2000. The risks of primary, reactivation, and exogenous re-infection disease among adults aged 25-44 years were 22%, 0.1%, and 6%, respectively. The number of transmissions per infectious case decreased from 16 to 2 from the early 1970s to the late 1990s. CONCLUSIONS: This study shows that the risk of infection among British Columbia First Nations people is decreasing, while the relative contribution of reactivation to disease incidence is increasing. Once infected, First Nations people may have a higher risk of developing disease than other populations.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".