Regularly scheduled physical examinations and the detection of breast cancer recurrences
Bibliographic record
Abstract
PURPOSE: Follow-up care of early breast cancer (EBC) patients usually includes routinely scheduled physical examinations. While ASCO guidelines recommend a physical exam every three to six months for the first three years, little evidence supports this schedule. We evaluated recurrence detection of patients transferred into a single centre survivorship program that follows ASCO recommendations. METHODS: Patients with EBC referred to the Wellness Beyond Cancer Program (WBCP) who had breast cancer recurrence between February 1, 2013, and January 1, 2019 were reviewed. Descriptive analyses were used to present patient and disease characteristics stratified by type of recurrence and mode of cancer detection. RESULTS: Of 206 recurrences, 135 were distant recurrences (65.5%), 41 were ipsilateral breast recurrences (19.9%), and 30 were contralateral breast primaries (14.6%). Distant recurrences were primarily detected via patient-reported symptoms (125/135, 92.6%). 53.7% (22/41) of ipsilateral breast recurrences were detected by patients and 41.5% (17/41) by routine imaging. Contralateral breast primaries were primarily detected by imaging 83.3% (25/30) and patient-reported symptoms 16.7% (5/30). Only 2/206 (1.14%) recurrences/new primaries were detected by healthcare providers at routinely scheduled follow-up visits. CONCLUSIONS: Despite following ASCO guidelines, healthcare providers rarely detect recurrences at routinely scheduled follow-up appointments. Our data suggests that approximately 35, 000 follow-up visits were required for healthcare providers to detect these 2 recurrences. While reduced in-person visits may affect other aspects of follow-up care (e.g. toxicity management), it appears unlikely, provided patients attend regular screening tests, that less frequent in-person follow-up is associated with worse breast cancer-related outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".