Breast density and risk of interval cancers: The effect of annual versus biennial screening mammography policies in Canada
Bibliographic record
Abstract
Regular screening mammography reduces breast cancer mortality. However, in women with dense breasts, the performance of screening mammography is reduced, which is reflected in higher interval cancer rates (ICR). In Canada, population-based screening mammography programs generally screen women biennially; however, some provinces and territories offer annual mammography for women with dense breast tissue routinely and/or on recommendation of the radiologist. This study compared the ICRs in those breast screening programs with a policy of annual vs. those with biennial screening for women with dense breasts. Among 148,575 women with dense breasts screened between 2008 to 2010, there were 288 invasive interval breast cancers; screening programs with policies offering annual screening for women with dense breasts had fewer interval cancers 63/70,814 (ICR 0.89/1000, 95% CI: 0.67-1.11) compared with those with policies of usual biennial screening 225/77,761 (ICR 1.45 /1000 (annualized), 95% CI: 1.19-1.72) i.e. 63% higher (p = 0.0016). In screening programs where radiologists' screening recommendations were able to be analyzed, a total of 76,103 women were screened, with 87 interval cancers; the ICR was lower for recommended annual (65/69,650, ICR 0.93/1000, 95% CI: 0.71, 1.16) versus recommended biennial screening (22/6,453, ICR 1.70/1000 (annualized), 95%CI: 0.70, 2.71)(p = 0.0605). Screening program policies of annual as compared with biennial screening in women with dense breasts had the greatest impact on reducing interval cancer rates. We review our results in the context of current dense breast notification 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".