Breast Radiation Therapy Under COVID-19 Pandemic Resource Constraints—Approaches to Defer or Shorten Treatment From a Comprehensive Cancer Center in the United States
Bibliographic record
Abstract
PURPOSE: Breast radiation therapy accounts for a significant proportion of patient volume in contemporary radiation oncology practice. In the setting of anticipated resource constraints and widespread community infection with SARS-CoV-2 during the COVID-19 pandemic, measures for balancing both infectious and oncologic risk among patients and providers must be carefully considered. Here, we present evidence-based guidelines for omitting or abbreviating breast cancer radiation therapy, where appropriate, in an effort to mitigate risk to patients and optimize resource utilization. METHODS AND MATERIALS: Multidisciplinary breast cancer experts at a high-volume comprehensive cancer center convened contingency planning meetings over the early days of the COVID-19 pandemic to review the relevant literature and establish recommendations for the application of hypofractionated and abbreviated breast radiation regimens. RESULTS: Substantial evidence exists to support omitting radiation among certain favorable risk subgroups of patients with breast cancer and for abbreviating or accelerating regimens among others. For those who require either whole-breast or postmastectomy radiation, with or without coverage of the regional lymph nodes, a growing body of literature supports various hypofractionated approaches that appear safe and effective. CONCLUSIONS: In the setting of a public health emergency with the potential to strain critical healthcare resources and place patients at risk of infection, the parsimonious application of breast radiation therapy may alleviate a significant clinical burden without compromising long-term oncologic outcomes. The judicious and personalized use of immature study data may be warranted in the setting of a competing mortality risk from this widespread pandemic.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".