What do women know about breast density? A public screening program perspective
Bibliographic record
Abstract
Abstract Background This study assesses knowledge of breast density, one of breast cancer’s strongest risk factors, in women attending a public mammographic screening program in Western Australia that routinely notifies women if they have dense breasts. Methods Survey data was collected from women who were notified they have dense breasts and women who had not (controls). Descriptive data analysis was used to summarize responses. Results Of the 6183 women surveyed, over 85% of notified women knew that breast density makes it difficult to see cancer on a mammogram (53.9% in controls). A quarter of notified women knew that having dense breasts puts women at increased risk for breast cancer (13.2% in controls). Overall, 50.1% of notified women indicated that they thought the amount of information provided was “just right” and 24.9% thought it was “too little”, particularly women notified for the first time (32.1%). Conclusion The main message of reduced sensitivity of mammography in women with dense breasts provided by the screening program appears to be getting though. However, women are largely unaware that increased breast density is associated with increased risk. Women notified of having dense breasts for the first time could potentially benefit from additional information.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".