Screening behaviours, demographics, and stage at diagnosis in the publicly funded Ontario Breast Screening Program
Bibliographic record
Abstract
PURPOSE: The Ontario Breast Screening Program (OBSP) offers free screening mammograms every 2 years, to women aged 50-74. Study objectives were to determine demographic characteristics associated with the adherence to OBSP and if women screened in the OBSP have a lower stage at diagnosis than non-screened eligible women. METHODS: We used the Ontario cancer registry (OCR) to identify 48,927 women, aged 51-74 years, diagnosed with breast cancer between 2010 and 2017. These women were assigned as having undergone adherent screening (N = 26,108), non-adherent screening (N = 6546) or not-screened (N = 16,273) in the OBSP. We used multinomial logistic regression to investigate the demographic characteristics associated with screening behaviour, as well as the association between screening status and stage at diagnosis. RESULTS: Among women with breast cancer, those living in rural areas (versus the largest urban areas) had a lower odds of not being screened (odds ratio [OR] 0.73, 95% confidence interval [CI] 0.68, 0.78). Women in low-income (versus high-income) communities were more likely not to be screened (OR 1.42, 95% CI 1.33, 1.51). When stratified, the association between income and screening status only held in urban areas. Non-screened women were more likely to be diagnosed with stage II (OR 1.91, 95% CI 1.82, 2.01), III (OR 2.96, 95% CI 2.76, 3.17), or IV (OR 8.96, 95% CI 7.94, 10.12) disease compared to stage I and were less likely to be diagnosed with ductal carcinoma in situ (DCIS) (OR 0.91, 95% CI 0.84-0.98). CONCLUSIONS: This study suggests that targeting OBSP recruitment efforts to lower income urban communities could increase screening rates. OBSP adherent women were more likely to be diagnosed with earlier stage disease, supporting the value of this initiative and those like it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".