Will COVID-19 directives to reduce regularly scheduled physical examinations affect recurrence detection in early breast cancer patients?
Bibliographic record
Abstract
Abstract Purpose: The COVID-19 pandemic resulted in a rapid move to virtual care. Questions exist as to whether reduced in-person assessment, with physical examination of early breast cancer patients (EBC), will affect the detection of recurrences. We evaluated recurrence patterns of patients transferred into a survivorship program through a single centre Wellness Beyond Cancer Program (WBCP).Methods: Consecutive EBC patients who returned to formal oncologist follow-up between February 1, 2013, and January 1, 2019, due to breast cancer recurrence were reviewed. Descriptive analyses were used to present patients and disease characteristics stratified by type of recurrence and mode of cancer detection.Results: Of 206 recurrences, 41 were ipsilateral breast recurrences (19.9%), 135 were distant recurrences (65.5%), and 30 were contralateral new breast cancers (14.6%). Ipsilateral breast recurrences were detected by patients in 53.7% (22/41) of cases and by routine imaging in 41.5% (17/21). The majority of distant recurrences (125/135, 92.6%) were detected via patient-reported symptoms. 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 follow-up guidelines, healthcare providers rarely detect recurrences at routine follow-up appointments. 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 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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".