Effect of Coronavirus Disease 2019 Pandemic on Delivery of Breast Reconstructive Services and Outcomes
Bibliographic record
Abstract
Background: Delivery of immediate breast reconstruction (IBR) was severely restricted during the coronavirus disease 2019 (COVID-19) pandemic, with irreversible consequences for breast cancer survivors. This study aimed to determine the pandemic’s effect on the provision of IBR services, quality of care delivered, and reconstructive outcomes. Methods: For this multi-institutional, retrospective cohort study, data were obtained from all IBR cases during the study periods defined as “pre-COVID-19” (October 1, 2018, to March 14, 2020) and “COVID-19” (March 15, 2020, to October 31, 2021). Patient demographics, reconstructive strategy types, and oncological and surgical characteristics were analyzed. Safety and quality outcomes, including readmission, infection, seroma, mastectomy flap necrosis, and wait times were recorded. Results: A cohort of 525 patients was included in this study. Patient and tumor characteristics were similar between the 2 study periods. There was a significantly lower odds of undergoing a single-stage alloplastic surgery (odds ratio [OR] = 0.40, 95% confidence interval [CI] = 0.17–0.94, P = 0.0365) or autologous immediate reconstruction with a deep inferior epigastric perforator (DIEP) flap (OR = 0.42, 95% CI = 0.21–0.85, P = 0.015) during the COVID-19 era, with a reciprocal 53% higher odds of 2-stage alloplastic surgery (OR = 1.53, 95% CI = 1.03-2.27, P = 0.0359) during that time. Median wait time from first-stage to second-stage reconstruction was significantly shorter during COVID-19 ( P = 0.0017). There were no differences in safety outcomes between the periods. Conclusions: Reconstructive strategies differed during the COVID-19 era of resource limitations with more 2-stage alloplastic procedures and fewer single-stage alloplastic and autologous immediate DIEP flap procedures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".