Accuracy of Self-Reported Screening Mammography Use: Examining Recall among Female Relatives from the Ontario Site of the Breast Cancer Family Registry
Bibliographic record
Abstract
Evidence of the accuracy of self-reported mammography use among women with familial breast cancer risk is limited. This study examined the accuracy of self-reported screening mammography dates in a cohort of 1,114 female relatives of breast cancer cases, aged 26 to 73 from the Ontario site of the Breast Cancer Family Registry. Self-reported dates were compared to dates abstracted from imaging reports. Associations between inaccurate recall and subject characteristics were assessed using multinomial regression. Almost all women (95.2% at baseline, 98.5% at year 1, 99.8% at year 2) accurately reported their mammogram use within the previous 12 months. Women at low familial risk (OR = 1.77, 95% CI: 1.00-3.13), who reported 1 or fewer annual visits to a health professional (OR = 1.97, 95% CI: 1.15, 3.39), exhibited a lower perceived breast cancer risk (OR = 1.90, 95% CI: 1.15, 3.15), and reported a mammogram date more than 12 months previous (OR = 5.22, 95% CI: 3.10, 8.80), were significantly more likely to inaccurately recall their mammogram date. Women with varying levels of familial risk are accurate reporters of their mammogram use. These results present the first evidence of self-reported mammography recall accuracy among women with varying levels of familial 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".