Lapses in breast cancer screening for highly penetrant mutation carriers during pregnancy and lactation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Screening for breast cancer in highly penetrant mutation carriers during pregnancy and lactation is challenging and consensus guidelines are lacking. This study evaluates the lapse in screening and the interval pregnancy-associated breast cancer rate. METHODS: A single-institution retrospective cohort study of pregnant and lactating patients with known pathogenic germline mutations was performed. Lapse in screening was defined as the interval between the last screening imaging exam obtained before last menstrual period and the subsequent screening imaging. RESULTS: Out of 685 patients, 42 had 1-3 evaluable pregnancies (54 total - 28 managed in High Risk Breast Clinic and 26 by OB/GYN). Mutations were observed in patients in BRCA1 (49%), BRCA2 (36%), CDH1 (5%), CHEK2 (2%), ATM (2%), NF1 (3%), and MSH2 (3%). The average screening lapse was 25 [19, 30] months for patients followed in the High Risk Clinic versus 32.5 [21, 65.75] months for patients followed with Routine Care (p = 0.035). We identified three cases of pregnancy-associated breast cancer (interval cancer rate 6%). CONCLUSIONS: Patients with highly penetrant mutations are at risk for the development of interval pregnancy-associated breast cancer. Development of consistent screening guidelines and adherence to those guidelines is needed for this patient population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".