Practice recommendations for risk-adapted head and neck cancer radiotherapy during the COVID-19 pandemic: An ASTRO-ESTRO consensus statement
Bibliographic record
Abstract
Purpose Because of the unprecedented disruption of health care services caused by the COVID-19 pandemic, the American Society of Radiation Oncology (ASTRO) and the European Society for Radiotherapy and Oncology (ESTRO) identified an urgent need to issue practice recommendations for radiation oncologists treating head and neck cancer (HNC) in a time of limited resources and heightened risk for patients and staff. Methods and Materials A panel of international experts from ASTRO, ESTRO, and select Asia-Pacific countries completed a modified rapid Delphi process. Topics and questions were presented to the group, and subsequent questions were developed from iterative feedback. Each survey was open online for 24 hours, and successive rounds started within 24 hours of the previous round. The chosen cutoffs for strong agreement (≥80%) and agreement (≥66%) were extrapolated from the RAND methodology. Two pandemic scenarios, early (risk mitigation) and late (severely reduced radiation therapy resources), were evaluated. The panel developed treatment recommendations for 5 HNC cases. Results In total, 29 of 31 of those invited (94%) accepted, and after a replacement 30 of 30 completed all 3 surveys (100% response rate). There was agreement or strong agreement across a number of practice areas, including treatment prioritization, whether to delay initiation or interrupt radiation therapy for intercurrent SARS-CoV-2 infection, approaches to treatment (radiation dose-fractionation schedules and use of chemotherapy in each pandemic scenario), management of surgical cases in event of operating room closures, and recommended adjustments to outpatient clinic appointments and supportive care. Conclusions This urgent practice recommendation was issued in the knowledge of the very difficult circumstances in which our patients find themselves at present, navigating strained health care systems functioning with limited resources and at heightened risk to their health during the COVID-19 pandemic. The aim of this consensus statement is to ensure high-quality HNC treatments continue, to save lives and for symptomatic benefit.
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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 | 0.054 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".