Association between the COVID-19 pandemic and first cancer treatment modality: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Physicians were directed to prioritize using nonsurgical cancer treatment at the beginning of the COVID-19 pandemic. We sought to quantify the impact of this policy on the modality of first cancer treatment (surgery, chemotherapy, radiotherapy or no treatment). METHODS: In this population-based study using Ontario data from linked administrative databases, we identified adults diagnosed with cancer from January 2016 to November 2020 and their first cancer treatment received within 1 year postdiagnosis. Segmented Poisson regressions were applied to each modality to estimate the change in mean 1-year recipient volume per thousand patients (rate) at the start of the pandemic (the week of Mar. 15, 2020) and change in the weekly trend in rate during the pandemic (Mar. 15, 2020, to Nov. 7, 2020) relative to before the pandemic (Jan. 3, 2016, to Mar. 14, 2020). RESULTS: We included 321 535 people diagnosed with cancer. During the first week of the COVID-19 pandemic, the mean rate of receiving upfront surgery over the next year declined by 9% (rate ratio 0.91, 95% confidence interval [CI] 0.88-0.95), and chemotherapy and radiotherapy rates rose by 30% (rate ratio 1.30, 95% CI 1.23-1.36) and 13% (rate ratio 1.13, 95% CI 1.07-1.19), respectively. Subsequently, the 1-year rate of upfront surgery increased at 0.4% for each week (rate ratio 1.004, 95% CI 1.002-1.006), and chemotherapy and radiotherapy rates decreased by 0.9% (rate ratio 0.991, 95% CI 0.989-0.994) and 0.4% (rate ratio 0.996, 95% CI 0.994-0.998), respectively, per week. Rates of each modality resumed to prepandemic levels at 24-31 weeks into the pandemic. INTERPRETATION: An immediate and sustained increase in use of nonsurgical therapy as the first cancer treatment occurred during the first 8 months of the COVID-19 pandemic in Ontario. Further research is needed to understand the consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".