Cancer Patients First Treated with Chemotherapy: Are They More Likely to Receive Surgery in the Pandemic?
Bibliographic record
Abstract
Due to the ramping down of cancer surgery in early pandemic, many newly diagnosed patients received other treatments first. We aimed to quantify the pandemic-related shift in rate of surgery following chemotherapy. This is a retrospective population-based cohort study involving adults diagnosed with cancer between 3 January 2016 and 7 November 2020 in Ontario, Canada who received chemotherapy as first treatment within 6-months of diagnosis. Competing-risks regression models with interaction effects were used to quantify the association between COVID-19 period (receiving a cancer diagnosis before or on/after 15 March 2020) and receipt of surgical reSection 9-months after first chemotherapy. Among 51,653 patients, 8.5% (n = 19,558) of them ultimately underwent surgery 9-months after chemotherapy initiation. Receipt of surgery was higher during the pandemic than before (sHR 1.07, 95% CI 1.02–1.13). Material deprivation was independently associated with lower receipt of surgery (least vs. most deprived quintile: sHR 1.11, 95% CI 1.04–1.17), but did not change with the pandemic. The surgical rate increase was most pronounced for breast cancer (sHR 1.13, 95% CI 1.06–1.20). These pandemic-related shifts in cancer treatment requires further evaluations to understand the long-term consequences. Persistent material deprivation-related inequity in cancer surgical access needs to be addressed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".