Willingness of breast cancer survivors to participate in a randomized controlled trial (RCT) of digital mammography with or without magnetic resonance imaging (MRI) as breast cancer (BC) surveillance: A feasiblity study
Bibliographic record
Abstract
1534 Background: Although no RCT of MRI screening has ever been performed, and thus its impact on survival is unknown, MRI (plus mammography) has become the standard of care for screening women at very high risk for BC due to its superior sensitivity. The current ASCO guideline for surveillance of women with a past history of BC patients for ipsilateral recurrence or contralateral BC is annual mammography; however, MRI is often being ordered as well. A RCT for this population is still theoretically feasible assuming one could recruit a sufficient number of patients. We conducted a study to evaluate the willingness of BC survivors to participate in a RCT of digital mammography with or without MRI. Methods: In this multi-center, cross-sectional cohort survey, consecutive women ages 20 to 59 with invasive BC who had completed all primary therapy (except hormonal therapy), had at least one remaining breast and no evidence of metastatic disease, were consented in clinic to complete a self-administered questionnaire. Results: 348 women at four study sites across Toronto, Canada, were enrolled over 10 months. Median age 51 years, 76% Caucasian, 69% had BC within the last 5 years, 37% had previous mastectomy. At diagnosis, 71% BC had been visible on mammogram; 45% had undergone diagnostic MRI. 22% continued to have MRI as part of surveillance imaging, especially women younger than 40 years. 58% agreed to consider participating in the proposed RCT, and 16% remained neutral. Women who had undergone mastectomy were more likely to consider the trial (71% vs. 52%). The most common reason for not considering the trial was that they were already getting surveillance MRI. Other reasons were additional false positive tests and biopsies (34%), discomfort associated with MRI (28%), and fear that their anxiety about cancer recurrence would increase (16%). Conclusions: A RCT of MRI surveillance for BC survivors younger than age 60 is still feasible at centers where annual MRI is not yet routinely recommended for this population. No significant financial relationships to disclose.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".