Mammographic screening and time to breast cancer diagnosis among immigrants and long-term residents in Ontario.
Bibliographic record
Abstract
e12572 Background: Breast Cancer is the most common in women globally and among Canadian women. We explored differences in screening rates and the time to breast cancer (BC) diagnosis among immigrants and long-term residents in Ontario). Methods: We calculated the annual proportion of Ontario women aged 50 – 75 up-to-date with mammography from January 2012 to 2020 and assessed trends using negative binomial regression, adjusting for immigration status, age, marginalization quintiles, and resource utilization. For BC diagnosis, we identified women aged 18-75 from the Ontario Cancer Registry (2012-2019). We matched long-term residents to immigrants by age, stage, and year at diagnosis and modelled time to diagnosis with linear regression. Results: The percentage of women up-to-date with mammographic screening ranged from 50% in 2012 to 52% in 2020 for immigrants and 61.5% in 2012 to 60.1% in 2020 for long-term residents. The Cochran-Armitage trend test over the study period was p = 0.79. The median time to diagnosis was 28 days (IQR 16-59) for long-term residents and 31 days (IQR 17-64) for immigrants. The range for both groups of women was 1-359 days, and over 10% of women in both had a diagnostic interval above 135 days (90 th percentile). We examined the stage distribution for the study population before matching and found that slightly more immigrants were diagnosed in stages two and three compared to long-term residents (Stage 2 (34.3% vs 37.4%), Stage 3(12.3% vs 14.3%) p = < .0001) and overall, more immigrants were diagnosed with stages 2,3 than long term residents (51.7% vs 46.6% p = < .001) but Stage 4 disease was similar for both groups (4.7% vs 5% p < 0.0001). Conclusions: Immigrants have significantly (26%) lower rates of up-to-date mammography and longer (1.2 days) diagnostic intervals. We found a slightly higher prevalence of stages two and three at diagnosis. The variation within both strata is enormous, but the difference in diagnostic interval is only 1.2 days. Multivariable models being up to date with mammography and multivariable linear regression model of time from first presentation to diagnosis. Multivariable models being up to date with mammography IRR (95% CI) Estimate (p-value) Immigrant statusYesNo 0.74(0.71-0.78)ref -0.30(<0.0001)ref Multivariable Linear regression model of time from first presentation to diagnosis Estimates (in days) (95% CI) P-value Immigrant statusYesNo 1.21(0.10, 2.32)ref 0.30ref
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".