Catching the Wave: Quantifying the Impact of COVID on Radiotherapy Delivery
Bibliographic record
Abstract
The novel coronavirus of 2019 has had a broad impact of the delivery of healthcare, including cancer care. We chose to quantify the impact in the radiation oncology department of the largest academic center in the hardest hit city in Canada. With the approval of our ethics review board, data on each patient treated from March 13, 2020 to August 10, 2020 were compared to patients treated during the same period in 2019. We compared the case mix, delay from treatment decision to treatment start, and number of fractions per patient. We reviewed prospectively collected information regarding deviations from our usual practice. During the pandemic the caseload was reduced by 12%; this was more pronounced in prostate and CNS tumors. The average number of fractions per patient was reduced from 12.3 to 10.9. This reduction was most marked in prostate, breast, gastro-intestinal, and palliative cases. When physicians were questioned, they reported that 17% of treatment plans deviated from their usual practice because of the pandemic. The most common deviations were related to changes in department policies (77%) vs. patient-specific deviations (20%) or changes requested by the patient (3%). Rare deviations were due to patients contracting COVID-19 (2 patients). At its worse, the wait list contained 27% of patients who had a delay to radiotherapy of more than 28 days. However, the average wait time increased little (19.6 days vs. 18.2 days) as more pressing cases were prioritized. In an unprecedented health crisis, our radiation oncology department was able to reduce resource utilization, notably by decreasing the number of fractions per patient. It will be important to follow these patients' health outcomes for insight into these practices. More quantitative tools to simulate and plan future practice changes in response to resource constraints will be implemented.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".