Predicting tuberculosis risk in the foreign-born population of British Columbia, Canada: study protocol for a retrospective population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: Improved understanding of risk factors for developing active tuberculosis (TB) will better inform decisions about diagnostic testing and treatment for latent TB infection (LTBI) in migrant populations in low-incidence regions. We aim to examine TB risk factors among the foreign-born population in British Columbia (BC), Canada, and to create and validate a clinically relevant multivariate risk score to predict active TB. METHODS AND ANALYSIS: This retrospective population-based cohort study will include all foreign-born individuals who acquired permanent resident status in Canada between 1 January 1985 and 31 December 2013 and acquired healthcare coverage in BC at any point during this period. Multiple administrative databases and disease registries will be linked, including a National Immigration Database, BC Provincial Health Insurance Registration, physician billings, hospitalisations, drugs dispensed from community pharmacies, vital statistics, HIV testing and notifications, cancer, chronic kidney disease and dialysis treatment, and all TB and LTBI testing and treatment data in BC. Extended proportional hazards regression will be used to estimate risk factors for TB and to create a prognostic TB risk score. ETHICS AND DISSEMINATION: Ethical approval for this study has been obtained from the University of British Columbia Clinical Ethics Review Board. Once completed, study findings will be presented at conferences and published in peer-reviewed journals. An online TB risk score calculator will also be created.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".