Late Breaking Abstract - Predictors of tuberculosis among immigrants referred for post-landing surveillance in British Columbia, Canada
Bibliographic record
Abstract
Background: Tuberculosis (TB) disproportionately affects immigrant populations in Canada. While The Immigration Medical Examinations (IMEs) prior to entry to Canada helps identify people at high TB risk, the predictive value of the IME for post-arrival TB disease remains unclear. Objective: We aimed to assess the association between clinical and demographic predictors recorded during the IME and the risk of developing TB disease within two years of arrival among people referred for post-landing surveillance in British Columbia (BC), Canada. Methods: We conducted a population-based, retrospective cohort study of 2,896 people referred for post-landing surveillance between 2020 and 2022. Using Cox proportional hazards models, we evaluated associations between baseline IME variables, such as chest x-ray findings, prior TB treatment, and country-level TB incidence, and time to TB disease diagnosis. Individuals were followed for two years, with TB diagnoses confirmed through BC’s provincial TB registry. Results: Among 2,896 participants, 31 (1.1%) developed TB disease within two years post-arrival. Abnormal chest x-ray was the strongest predictor: 97% of diagnosed individuals had abnormal imaging at IME. The TB incidence rate among those with abnormal chest x-rays was 656 per 100,000 person-years, compared to 85 per 100,000 for those with normal radiographs. In multivariable Cox regression, abnormal x-ray findings were significantly associated with higher TB risk (HR:2.14;95% CI:1.20-3.82;p=0.01). Conclusion: Abnormal chest x-ray findings during the IME help predict progression to TB disease within two years of arrival. Prioritizing post-landing follow-up may improve early detection.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".