Annual Mammographic Screening Reduces the Risk of Interval or Higher Stage Invasive Breast Cancers Among Postmenopausal Women in the Ontario Breast Screening Program
Bibliographic record
Abstract
Purpose: In the Ontario Breast Screening Program (OBSP) annual screening improved breast cancer detection for women 50–74 years with a family/personal history compared to biennial, while detection was equivalent for women screened annually for mammographic density ≥75%. This study compares the risk of interval or higher stage invasive cancers among postmenopausal women screened annually vs biennially by age and estrogen use. Methods: A retrospective design identified 4247 invasive breast cancers diagnosed among concurrent cohorts of women 50–74 screened in the OBSP with digital mammography between 2011 and 2014, followed until 2016. Polytomous logistic regression estimated the risk of interval or higher stage breast cancers by age and estrogen use between women screened annually because of first-degree relative with breast or ovarian cancer or personal history of ovarian cancer, or mammographic density ≥75%, and those screened biennially. Results: The risk of interval vs screen-detected cancers was significantly reduced in women screened annually for family/personal history (OR=.64; 95%CI:0.51–.80), particularly those 60–74 years (OR=.59; 95%CI:0.45–.77) or not currently using estrogen (OR=.66; 95%CI:0.52–.83) compared to those screened biennially. The risk of stage II–IV vs stage I tumors was also lower in women 60–74 years screened annually for family/personal history (OR=.79; 95%CI:0.64–.97) and in those screened annually for mammographic density ≥75% currently using estrogen (OR=.51; 95%CI:0.26–1.01) compared to women screened biennially. Conclusion: Postmenopausal women at increased risk screened annually had equivalent or reduced risks of interval or higher stage invasive breast cancers than those screened biennially, further supporting risk-based screening in this population.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".