Risk of Invasive Cervical Cancer Among Immigrants in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The risk of invasive cervical cancer (ICC) varies throughout the world. We aimed to compare the risk of this invasive disease among immigrants arriving in Ontario with that of the general female population of Ontario. METHODS: We used an exposure-control matched design. We identified females from the Immigration, Refugees, and Citizenship Canada (IRCC) database with arrival in Ontario, and whose first eligibility for the Ontario Health Insurance Plan according to its Registered Persons Database fell between July 1, 1991, and June 30, 2008, at age 20 years or older, and matched two female controls on year of birth. We identified cases of ICC between the index date and December 31, 2014. Crude rates and relative rates of ICC were calculated. Multivariable extended Cox regression models were then implemented. RESULTS: The crude rate of ICC was 0.032 per 100 000 person-years for immigrants and 0.037 for controls. Immigrants who were born in certain countries showed a higher risk of ICC; Russia had a relative rate of 1.736 compared with a relative rate of 0.221 among those born in Iran. Among immigrants, the age-adjusted HR was 0.76 (95% CI 0.63-0.92) after 10 years of residency when compared with controls. Immigrants aged 20 to 39 years had a lower risk of ICC compared with controls of equivalent age, and immigrants aged ≥40 years had a higher risk of ICC. CONCLUSIONS: The risk of ICC among immigrants in Ontario varies by age, country of birth, and time since immigration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".