Will COVID-19 directives to reduce regularly scheduled physical examinations affect recurrence detection in patients with early breast cancer?
Bibliographic record
Abstract
1532 Background: The COVID-19 pandemic has significantly reduced routinely scheduled in person assessment and examination of early breast cancer patients (EBC). To assess if this is likely to impact the detection of recurrent disease, we reviewed recurrence patterns of EBC patients enrolled in a survivorship program that adheres to ASCO guidelines. Methods: Charts of EBC patients transferred through a single center Wellness Beyond Cancer Program (WBCP) and who subsequently had a breast cancer recurrence between February 1, 2013 and January 1, 2019 were reviewed. Patient, tumor and treatment characteristics were evaluated. Results: Of 206 patients eligible for the current study, 41 patients had ipsilateral breast recurrences (19.9%), 135 had distant recurrences (65.5%) and 30 had contralateral new breast cancers (14.6%). Ipsilateral breast recurrences were detected by the patient in 53.7% (22/41) 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 detected by patients 16.7% (5/30) or by routine imaging (83.3%, 25/30). Only 2/206 (1.14%) recurrences/new primaries were detected by healthcare providers at routinely scheduled follow-up visits. There was a statistical difference in recurrence detection between image detected vs. self-detected in the following factors: grade 3 (26.5% vs 51%, p < 0.007), triple negative breast cancer (3.9% vs. 15.1%, p = 0.03), HER2 disease (18.4% vs. 9.8%, p = 0.04). Conclusions: Despite following ASCO follow-up guidelines for routinely scheduled follow-up appointments with physical examination, healthcare providers rarely detect recurrence disease. While reduced in person visits may affect other aspects of follow-up (e.g., toxicity management), it appears unlikely, provided patients attend regular screening tests, that reduced in-person follow-up is associated with worse breast cancer-related outcomes during the COVID-19 pandemic. [Table: see text]
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".