Rapid Adaptation of Breast Radiation Therapy Use During the Coronavirus Disease 2019 Pandemic at a Large Academic Cancer Center in Canada
Bibliographic record
Abstract
PURPOSE: Mitigation strategies to balance the risk of coronavirus disease 2019 (COVID-19) infection against oncologic risk in patients with breast cancer undergoing radiation therapy have been deployed. To this end, shorter hypofractionated regimens have been recommended where appropriate, with prioritization of radiation therapy by oncologic risk and omission or deferral of radiation therapy for lower risk cases. Timely adoption of these measures reduces COVID-19 risk to both patients and health care workers and preserves resources. Herein, we present our early response and adaptation of breast radiation therapy utilization during the COVID-19 pandemic at a large academic cancer center in Canada. METHODS AND MATERIALS: A state of emergency was announced in Ontario on March 17, 2020, owing to the COVID-19 pandemic. Emergency guidelines were instituted. To examine our response, the number of weekly breast radiation therapy starts, type of breast radiation therapy, and patient age were compared from March 1 to April 30, 2020 to the same period in 2019. RESULTS: = .0027. CONCLUSIONS: A significant reduction in breast radiation therapy starts was noted during the early response to the COVID-19 pandemic, with prioritization of radiation therapy to patients associated with higher oncologic risk requiring RNI. A quick response to a health care crisis is critical and is of particular importance for higher volume cancer sites where the potential effect on resources is greater.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".