<p>Mammographic density parameters and breast cancer tumor characteristics among postmenopausal women</p>
Bibliographic record
Abstract
Purpose: Mammographic density is an important breast cancer risk factor, although it is not clear whether the association differs across breast cancer tumor subtypes. We examined the association between indicators of mammographic density and breast cancer risk by tumor subtype among postmenopausal women by investigating heterogeneity across tumor characteristics. Methods: Mammographic density measures were determined for 477 breast cancer cases and 588 controls, all postmenopausal, in Vancouver, British Columbia, using digitized screening mammograms and Cumulus software. Mammographic dense (DA), non-dense (NDA), and percent dense (PDA) areas were treated as continuous covariates and categorized into quartiles according to the distribution in controls. For cases only, tests for heterogeneity between tumor subtypes were assessed by multinomial logistic regression. Associations between mammographic density and breast cancer risk were modeled for each subtype separately through unconditional logistic regression. Results: Heterogeneity was apparent for the association of PDA with tumor size ( p -heterogeneity=0.04). Risk did not differ across the other assessed tumor characteristics ( p -heterogeneity values >0.05). Conclusion: These findings do not provide strong evidence that mammographic density parameters differentially affect specific breast cancer tumor characteristics. Keywords: mammographic density, breast cancer, tumor characteristics, heterogeneity, multinomial logistic regression
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".