Influence of perceived breast cancer risk on screening behaviors of female relatives from the Ontario site of the Breast Cancer Family Registry
Bibliographic record
Abstract
Few studies have examined the influence of perceived risk on breast screening behaviors among women with an increased familial breast cancer risk. This study included 1019 women aged 20-71 years from the Ontario site of the Breast Cancer Family Registry who had at least one first-degree relative diagnosed with breast and/or ovarian cancer. Information was obtained from a self-administered questionnaire completed at the time of recruitment and a follow-up telephone questionnaire. The associations between breast screening behaviors and perceived risk of developing breast cancer, measured on both a numerical and Likert-type verbal scale, were estimated using logistic regression analyses. Women who rated their risk of developing breast cancer as greater than 50% compared with less than 50% were significantly more likely to have a screening mammogram within the last 12 months (odds ratio: 1.91; 95% confidence interval: 1.15-3.16). Women were significantly more likely to have a screening mammogram (odds ratio: 1.82; 95% confidence interval: 1.17-2.81) in the past 12 months if they rated their risk as above or much above average compared with same as average or below. These findings may inform educational messages for improving risk communication of women at elevated familial breast cancer risk.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".