Influence of BI-RADS Breast Density Scores on the Implementation of Supplemental Imaging Modalities in Those With Average Risk and Negative Mammogram by Primary Care Providers in British Columbia
Bibliographic record
Abstract
Introduction: Breast Imaging-Reporting and Data System (BI-RADS) density scores have been included in screening mammography reports in BC since 2018. Despite these density scores being present in screening mammography reports for numerous years, there remains insufficient evidence to guide supplemental testing for patients with dense breasts. Objective: The primary objective of this study was to evaluate how primary care providers in Canada utilize BI-RADS density scores reported on normal screening mammograms of average risk, asymptomatic patients in their clinical practice. The secondary objective of this study was to determine if there are any patterns related to primary care provider demographics and practice settings in BC that could be linked to differences in screening practices for patients based on BI-RADS density scores. Methods: A cross-sectional survey was conducted with family physicians (FPs) and nurse practitioners (NPs) practicing in BC. Descriptive statistics were calculated using percentages and further stratified by participant demographics. P values were derived from Fisher’s exact test and results were regarded as statistically significant at P < .05. Results: Ninety-eight participants (85 FPs, 13 NPs) responded to the survey. The percentage of participants who ordered supplemental testing based on BI-RADS density scores alone was 8% for BI-RADS score D, 37% for BI-RADS scores C or D, and 2% for BI-RADS scores B, C, or D. Forty-eight percent of female participants and 45% of male participants would order supplemental testing based on BI-RADS density scores alone ( P = 1). Forty-nine percent of FPs and 39% of NPs would order supplemental testing based on BI-RADS density scores ( P = .56). Fifty-three percent of participants who had been in practice for more than 10 years, 50% of those who had been in practice for 6 to 10 years, and 36% of those in practice for 5 years or less would order supplemental testing ( P = .34). Fifty-seven percent of those practicing in large urban centres, 43% of those practicing in medium-sized communities, and 32% of those in rural or remote communities would order testing ( P = .17). Fifty-seven percent of participants were aware of the increased risk of breast cancer with higher breast density. Conclusion: Variations exist in how primary care providers in BC utilize the BI-RADS density scores reported on normal screening mammography of average risk, asymptomatic patients in their clinical practice. Further research in this area is needed to establish clearer clinical guidelines to educate and inform primary care providers on the need for supplemental testing for patients with dense breasts and to improve resources for breast cancer screening in BC.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".