The Impact of Organised Screening Programs on Breast Cancer Stage at Diagnosis for Canadian Women Aged 40–49 and 50–59
Bibliographic record
Abstract
The relationship between Canadian mammography screening practices for women 40−49 and breast cancer (BC) stage at diagnosis in women 40−49 and 50−59 years was assessed using data from the Canadian Cancer Registry, provincial/territorial screening practices, and screening information from the Canadian Community Health Survey. For the 2010 to 2017 period, women aged 40−49 were diagnosed with lesser relative proportions of stage I BC (35.7 vs. 45.3%; p < 0.001), but greater proportions of stage II (42.6 vs. 36.7%, p < 0.001) and III (17.3 vs. 13.1%, p < 0.001) compared to women 50−59. Stage IV was lower among women 40−49 than 50−59 (4.4% vs. 4.8%, p = 0.005). Jurisdictions with organised screening programs for women 40−49 with annual recall (screeners) were compared with those without (comparators). Women aged 40−49 in comparator jurisdictions had higher proportions of stages II (43.7% vs. 40.7%, p < 0.001), III (18.3% vs. 15.6%, p < 0.001) and IV (4.6% vs. 3.9%, p = 0.001) compared to their peers in screener jurisdictions. Based on screening practices for women aged 40−49, women aged 50−59 had higher proportions of stages II (37.2% vs. 36.0%, p = 0.003) and III (13.6% vs. 12.3%, p < 0.001) in the comparator versus screener groups. The results of this study can be used to reassess the optimum lower age for BC screening in Canada.
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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 | 0.005 |
| 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 | 0.001 |
| 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".