Influence of Patterns of Hormone Replacement Therapy Use and Mammographic Density on Breast Cancer Detection
Bibliographic record
Abstract
BACKGROUND: There is evidence that factors such as current hormone replacement therapy (HRT) use and mammographic density may each lower the sensitivity of mammography and are associated with a greater risk of developing an interval cancer. This study explores this relationship further by examining the influence of patterns of HRT use and the percentage of mammographic density on the detection of breast cancer by classification of interval cancer. METHODS: This study uses a case-case design nested within a cohort of women screened by the Ontario Breast Screening Program between 1994 and 2002. Interval cancers, both those missed at screening but seen on retrospective review (n = 87) or true intervals without visible tumor signs at screening (n = 288) were matched to 450 screen-detected cancers. The association between the percentage of mammographic density, measured by radiologists and a computer-assisted method, and HRT use, ascertained from a mailed questionnaire, and the risk of being diagnosed with an interval cancer was estimated using conditional logistic regression. RESULTS: A monotonic gradient of increasing risk for interval cancers was found for each 25% increase in mammographic density [odds ratio (OR), 1.77; 95% confidence intervals (95% CI), 1.07-2.95 for missed intervals and OR, 2.16; 95% CI, 1.59-2.94 for true intervals]. After adjusting for mammographic density, a significantly increased risk for true-interval cancers remained for women taking estrogen alone (OR, 1.75; 95% CI, 1.11-2.83) as well as for missed- (OR, 2.84; 95% CI, 1.32-6.13) and true-interval cancers (OR, 1.79; 95% CI, 1.10-2.90) for women taking combined HRT. CONCLUSIONS: Information on mammographic density and HRT use should routinely be collected at the time of screening. Women at risk should be made aware of the lower sensitivity of mammography and offered alternative procedures for screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".