Differences in Measured Mammographic Density in the Menstrual Cycle
Bibliographic record
Abstract
BACKGROUND: In premenopausal women, the sensitivity of screening mammography for detecting breast cancer has been reported to be greater in the follicular phase than in the luteal phase of the menstrual cycle, which may be due to differences in mammographic density. To examine this possible effect, we compared mammographic density in premenopausal women who had mammograms at different phases of the menstrual cycle. METHODS: We recruited premenopausal women ages 40 to 49 years from two mammography units in Toronto, recorded the first day of the last menstrual period (LMP) and measured mammographic density using Cumulus software. We classified the time of the mammography examination as having occurred in one of four intervals, 1 (first week after LMP), 2 (second week after LMP), 3 (third week after LMP) and 4 (>3 weeks after LMP), and compared mammographic density across intervals. RESULTS: Of the 936 women included in the analysis, 620 were examined by film and 316 by digital mammography. There were small and statistically nonsignificant variations in breast dense, nondense area, and percent density over the menstrual cycle in women examined by film mammography. Marginally significant variation in percent density was observed in the digital subset due to significant differences in the amount of nondense tissue over the menstrual cycle. CONCLUSION: Variations in mammographic density over the menstrual cycle were small and nonsignificant for women examined by either film or digital mammography. Thus, timing of mammography in menstrual cycle is unlikely to have a significant influence in breast cancer detection by screening mammography.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".