Impact of switching from digital mammography to tomosynthesis plus digital mammography on breast cancer screening in Alberta, Canada
Bibliographic record
Abstract
Objectives To compare abnormal call rates (ACR), cancer detection rates (CDR), positive predictive values (PPVs), and annual return to screen recommendations after switching from digital mammography (DM) to digital breast tomosynthesis plus DM (DBT + DM) for breast cancer screening. Setting The Alberta Breast Cancer Screening Program collects screening data from clinics throughout the province of Alberta, Canada. Methods This study retrospectively collected data, between 2015 and 2018, on women aged 40+ who underwent breast cancer screening at two large volume multisite radiology groups to compare metrics one year prior and one year after DBT + DM implementation. Comparisons between modalities were carried out within age groups, within breast density categories, and for initial vs. subsequent screens. Results A total of 125,432 DM and 128,912 DBT + DM screening exams were performed. For women aged 50–74, the DBT + DM group had a higher ACR ( p < 0.01) but lower annual return to screens ( p < 0.01). CDR was higher post-DBT + DM implementation for women with scattered (6.0 per 1000 vs. 4.4 per 1000; p = 0.001) or heterogeneously dense breasts (6.5 per 1000 vs. 4.2 per 1000; p < 0.001). PPV was higher with DBT + DM for all age groups, with women 50–74 having a PPV of 8.3% using DBT + DM vs. 7.1% with DM ( p = 0.009). Conclusion All metrics improved or stayed the same after switching to DBT + DM except for ACR. However, the increase in ACR could be attributed to a trend already occurring prior to the switch. Longer term monitoring is needed to confirm these findings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".