COVID-19 Pandemic’s Effects on Breast Cancer Screening, Staging at Diagnosis at Presentation, Oncologic Management, and Immediate Reconstruction: A Canadian Perspective
Bibliographic record
Abstract
BACKGROUND: Did the COVID-19 pandemic lead to delays in breast cancer management, impacting treatment recommendations? The goal of this study was to assess the pandemic's effect on breast cancer treatment and management practices. METHODS: This study aimed to assess the pandemic's effect on breast cancer treatment from March 2018 to February 2020 (pre-pandemic) and March 2020 to February 2022 (during the pandemic) in Canada. A retrospective cohort study at The Ottawa Hospital, Ontario, Canada, compared breast cancer patients diagnosed in the two years before and after the pandemic's onset. The study examined patient demographics, cancer stages, treatment timelines, and procedures, including neoadjuvant chemotherapy, endocrine therapy, and surgical treatment. Descriptive statistics and frequencies identified changes. The study is limited to a single institution, which may restrict generalizability. Inclusion criteria focused on female patients over 18 years with newly diagnosed breast cancer, excluding recurrent cases. Stage IV patients were included, but further details on their management are needed. RESULTS: < 0.001). The study revealed a decrease in breast cancer diagnoses and surgeries during the pandemic, with a rise in non-surgical treatments. CONCLUSIONS: These changes indicate significant shifts in breast cancer management due to the pandemic. The decrease in surgical treatments and increase in non-surgical options such as endocrine therapy and radiotherapy suggest adaptations in clinical practices to cope with the challenges posed by the pandemic. Understanding these shifts is crucial for developing strategies to mitigate the impact of future disruptions on breast cancer care and ensuring optimal patient 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".