Abstract PO3-23-02: Delays in breast cancer surgery during the COVID-19 pandemic: A population-based analysis
Bibliographic record
Abstract
Abstract BACKGROUND: At various times during the COVID-19 pandemic, access to operating room resources were restricted in Ontario, Canada. This resulted in triaging of all non-urgent cases, including breast cancer (BC) surgery. Recent single institution studies have identified treatment delays during the immediate pandemic time period. The objective of this study was to assess the impact of the pandemic on wait times for breast cancer surgery at a population level. METHODS: We identified patients with a BC diagnosis between January 1, 2018 and November 30, 2021 in Ontario, Canada. Three cohorts were defined based on the following time periods: pre-pandemic (baseline) (January 2018 to 14 March 2020), immediate pandemic (March 15, 2020 to June 13, 2020), and peri-pandemic (June 14, 2020 to November 30, 2021). Time to event analysis was conducted for time to a patient’s first BC surgery. A subgroup analysis was conducted for patients who underwent neoadjuvant chemotherapy (NAC). RESULTS: The study cohort consisted of 36,639 patients with a new diagnosis of BC. Of them 30,862 patients underwent surgery (84.2%) within 18 months after diagnosis. The probability of BC patients undergoing surgery each month from their date of diagnosis was significantly lower (i.e. longer wait times) for patients diagnosed in the immediate and peri-pandemic period compared to patients diagnosed in the pre-pandemic period (log-rank P < 0.001). At eight weeks from date of diagnosis, the probability of having had surgery was 65.1% (95% CI: 64.5-65.8%) for patients diagnosed in the pre-pandemic period, 56.1% (95% CI: 53.6-58.6%) for immediate pandemic and 59.0% (95% CI: 58.2-59.8%) for peri-pandemic. Time for 80% of patients to have BC surgery was 5.4 months in the pre-pandemic, 5.9 months in the immediate pandemic, and 6.0 months in the peri-pandemic period. Among patients who received NAC (N=6,121; 16.7% of cohort), the probability of undergoing surgery in each month from the date of diagnosis was similar for patients diagnosed in the pre and peri-pandemic periods, but was higher (i.e. shorter wait time) for the cohort of patients diagnosed during the immediate pandemic time period (log-rank P < 0.001). At six months from date of diagnosis, the probability of surgery for patients who received NAC was 50.7% (95% CI: 48.9-52.5%), 61.6% (95% CI: 56.6-66.6%), and 50.9% (95% CI: 49.0-52.8%) for patients diagnosed in the pre-, immediate, and peri-pandemic periods respectively. Time for 80% NAC patients to have a surgery was 7.2 months in the immediate pandemic period, shorter than the 8.2 months in the pre-pandemic and 8.0 months in the peri-pandemic periods. CONCLUSIONS: In this large population-based study of BC patients in Ontario, we identified a delay in time to surgery in the immediate pandemic period that persisted into the peri-pandemic period. However, among patients receiving NAC there was a shorter wait time during the immediate pandemic and wait times for this group have returned to pre-pandemic levels. This is likely due to surgical prioritization of this group, despite pandemic related resource restrictions. Further research is needed to explore the regional variability and how use of neoadjuvant endocrine therapy interacted with surgical treatment timelines. Citation Format: Gary Ko, Amanda Roberts, Qing Li, Ning Liu, Toni Zhong, Eitan Amir, Anne Koch, Andrea Covelli, Vivianne Freitas, Emma Reel, Antoine Eskander, Tulin Cil. Delays in breast cancer surgery during the COVID-19 pandemic: A population-based analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-23-02.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".