Menses and breast cancer: Does timing of mammographically directed core biopsy affect outcome?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Studies have shown molecular, genetic and cellular changes in breast cancer during the menstrual cycle. Changes in proliferative and metastatic potential of breast cancer cells during menses could explain improved survival when tumors are surgically removed in the luteal phase. This study examined if timing of mammography/core biopsy (MAM-CB) also affected breast cancer prognosis (histological tumor grade). METHODS: Eighty-five premenopausal women undergoing MAM-CB at one clinic between March 1995 and February 1998 were retrospectively studied. All patients had Stage I or II breast cancer surgically treated. Patients were grouped by phase of menses at MAM-CB: follicular (F, Days 0-14) or luteal (L, Days 15-35). Groups were comparable in age, menarche, family history, nulliparity, breastfeeding, and total percentage of clinically palpable tumors. Pathological characteristics of the tumors (tumor size, tumor type, estrogen and progesterone receptor status, axillary lymph node status, the presence of lymphatic or vascular invasion and extranodal metastasis) was also comparable across the 2 groups. RESULTS: Low-grade tumors were more frequent in the MAM-CB group L, whereas high-grade tumors were more common in the MAM-CB group F (P = 0.002, chi2(4) = 17.06). CONCLUSIONS: Timing of MAM-CB in relation to menses may be a factor influencing breast cancer outcome. Future studies examining the effect of menses on the outcome of breast cancer should consider the potential effect of the timing of MAM-CB.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".