Impact of the COVID-19 pandemic on breast cancer surgeries in a Canadian population
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic significantly impacted breast cancer (BC) surgeries. Most studies showing reduced BC surgical volumes during the pandemic are from single institutions, few have described volume changes in different types of surgical procedures. This study aimed to assess the impact of the pandemic on BC surgery volumes and types at a population level. METHODS: Patients diagnosed with BC between January 1, 2018, and June 25, 2022, in Ontario, Canada, were analysed from population-based datasets. Time periods were defined as pre-pandemic (Jan 2018-Mar 2020), immediate pandemic (Mar-Jun 2020), and peri-pandemic (Jun 2020-Jun 2022). Weekly BC surgery volume and type (lumpectomy, mastectomy, or mastectomy with immediate reconstruction) were evaluated using segmented negative binomial regression models. RESULTS: Among 44 226 patients, 50 440 surgeries were performed. Weekly BC surgeries decreased by 16.9% during the immediate pandemic compared to pre-pandemic levels (180.5 vs. 217.1; p = 0.03). Surgical volumes recovered to pre-pandemic levels by June 2021. Mastectomies represented a higher proportion of BC surgeries during the pandemic (31.1% pre, 36.3% immediate, 32.4% peri-pandemic; p < 0.01). The proportion of mastectomies with immediate reconstruction remained stable during the immediate pandemic but increased in the peri-pandemic (20.1% vs. 17%; p < 0.01). CONCLUSION: There was a significant reduction in all BC surgeries during the pandemic. Mastectomies accounted for a higher proportion of BC surgeries in the pandemic period however access to reconstruction was maintained. Surgical volumes recovered within a year despite ongoing pandemic hospitalizations. Future studies are needed to explore the pandemic's long-term impact on BC care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".