Canadian Society Breast Imaging Position Statement on Mammographic Breast Density and Supplemental Screening
Bibliographic record
Abstract
Screening aims to detect breast cancer before it becomes clinically apparent, enabling identification of tumors when they are smaller and have not yet spread and when treatment options are more effective, less invasive, and more affordable. However, screening mammography has known limitations, with breast density being a primary challenge. Denser breast tissue not only increases the likelihood of cancer but also makes tumors harder to detect due to overlapping tissue. Strong evidence now exists to support updating our previous guidelines to recommend supplemental screening beyond mammography for individuals with American College of Radiology category c or d breast density. Supplemental screening methods, such as MRI, contrast-enhanced mammography (CEM), or US (in that order of preference) can significantly improve cancer detection rates. We recognize that implementing these recommendations across Canada will present challenges. Nevertheless, a collaborative effort among radiologists, health care stakeholders, and policymakers is essential to drive gradual, meaningful improvements in breast cancer detection and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".