Impact of COVID-19 on Radiation Oncology, an Austrian Experience
Bibliographic record
Abstract
The COVID-19 pandemic has an unprecedented impact on cancer treatment worldwide. We aimed to evaluate the effects of the pandemic on the radiation treatment of patients in order to provide data for future management of such crises. We compared the number of performed radiotherapy sessions of the pandemic period from February 2020 until May 2021 with those of 2018 and 2019 for reference. At our department, no referred patients had to be rejected or postponed, nor any significant changes in fractionation schedules implemented. Nevertheless, there was a substantial drop in overall radiotherapy sessions in 2020 following the first incidence wave of up to -25% (in June) in comparison to previous years. For breast cancer, a maximum decline of sessions of -45% (July) was recorded. Only a short drop of prostate cancer sessions (max -35%, May) followed by a rebound (+42%, July) was observed. Over the investigated period, a loss of 4.4% of expected patients never recovered. The severe impact of COVID-19 on cancer treatment, likely caused by retarded diagnosis and delayed interdisciplinary co-treatment, is reflected in a lower count of radiotherapy sessions. Radiation oncology is a crucial cornerstone in upholding both curative treatment options and treatment capacity during a 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".