Early survival for patients newly diagnosed with cancer during <scp>COVID</scp>‐19 in Ontario, Canada: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Little is known about the association between the COVID-19 pandemic and early survival among newly diagnosed cancer patients. METHODS: This retrospective population-based cohort study used linked administrative datasets from Ontario, Canada. Adults (≥18 years) who received a cancer diagnosis between March 15 and December 31, 2020, were included in a pandemic cohort, while those diagnosed during the same dates in 2018/2019 were included in a pre-pandemic cohort. All patients were followed for one full year after the date of diagnosis. Cox proportional hazards regression models were used to assess survival in relation to the pandemic, patient characteristics at diagnosis, and the modality of first cancer treatment as a time-varying covariate. Interaction terms were explored to measure the pandemic association with survival for each cancer type. RESULTS: Among 179,746 patients, 53,387 (29.7%) were in the pandemic cohort and 37,741 (21.0%) died over the first post-diagnosis year. No association between the pandemic and survival was found when adjusting for patient characteristics at diagnosis (HR 0.99 [95% CI 0.96-1.01]), while marginally better survival was found for the pandemic cohort when the modality of treatment was additionally considered (HR 0.97 [95% CI 0.95-0.99]). When examining each cancer type, only a new melanoma diagnosis was associated with a worse survival in the pandemic cohort (HR 1.25 [95% CI 1.05-1.49]). CONCLUSIONS: Among patients able to receive a cancer diagnosis during the pandemic, one-year overall survival was not different than those diagnosed in the previous 2 years. This study highlights the complex nature of the COVID-19 pandemic impact on cancer care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".