The Impact of the COVID-19 Pandemic on Breast Reconstruction: A Canadian Perspective
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led to unprecedented challenges and restrictions in surgical access across Canada, including for breast reconstructive services which are an integral component of comprehensive breast cancer care. We sought to determine how breast reconstructive services are being restricted, and what strategies may be employed to optimize the provision of breast reconstruction through a pan-Canadian evaluation from the providers' perspective. METHODS: This was a cross-sectional survey of Canadian plastic and reconstructive surgeons who perform breast reconstruction. The 33-item web-based questionnaire was developed by a pan-Canadian working group of breast reconstruction experts and disseminated via email to members of the Canadian Society of Plastic Surgery. The questionnaire queried respondents on the impact of the COVID-19 pandemic and associated restrictions on surgeons' breast reconstruction practice patterns and opinions on strategies for resource utilization. RESULTS: Responses were received from 49 surgeons, who reported practicing in 8 of 10 Canadian provinces. Restrictions on the provision of breast reconstructive procedures were most limited during the First Wave of the COVID-19 pandemic, where all respondents reported at least some reduction in capacity and more than a quarter reporting complete cessation. Average reported reduction in capacity ranged from 31% to 78% across all 3 waves. Autologous, delayed, and prophylactic reconstructions were most commonly restricted. CONCLUSION: This study provides a pan-Canadian impact assessment on breast reconstructive services during the COVID-19 pandemic from the providers' perspective. To uphold the standards of patient-centred care, a unified approach to strategically reorganize health care delivery now and in the future is needed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".