Immigration, region of origin, and the epidemiology of venous thromboembolism: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) epidemiology has been mainly studied in populations largely of European ancestry. OBJECTIVES: To assess the epidemiology of VTE in immigrants to Ontario, Canada. PATIENTS/METHODS: We conducted a population-based retrospective cohort study using linked health-care and administrative databases. We included 1 195 791 immigrants to Ontario and 1 195 791 nonimmigrants, matched on age, sex, and place of residence. The main exposure was ethnicity according to world region of origin, using a previously validated algorithm. The main outcome was incident onset of VTE during the period of observation. Risk ratios (RR) were calculated using Poisson regression models. RESULTS: The incidence rate (IR) of VTE was lower among immigrants (0.87 per 1000 PY; 95% confidence interval [CI] 0.85-0.89) than nonimmigrants (1.59 per 1000 PY; 95% CI 1.56-1.61). Age- and sex-standardized IR were lower among East and South Asian immigrants. Compared to immigrants for predominantly White regions, age- and sex-specific RRs were consistently lower for East Asian (0.18-0.30) and South Asian (0.29-0.75) immigrants. In contrast, the RRs of VTE among Black (0.38-1.50), Latin American (0.29-1.25), Arab/Middle Eastern (0.44-1.08) and West Asian (0.31-1.16) immigrants were not significantly different from White immigrants. CONCLUSIONS: In Ontario, the incidence of VTE is lower among immigrants compared to nonimmigrants. East and South Asian immigrants have a lower risk of VTE compared to White immigrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".