Cancer survival during the COVID-19 pandemic in the International Cancer Benchmarking Partnership
Bibliographic record
Abstract
Abstract Introduction While cancer survival has improved over decades, the COVID-19 pandemic introduced challenges to cancer care. This study compares 1-year survival among patients diagnosed in 2020 with those diagnosed in 2018-2019. Methods We obtained patient-level data for colon, rectal, lung, breast, and ovarian cancer diagnosed in 2018-2020 from Australia, Canada, Ireland, New Zealand, and the UK, with follow-up to 2021. We included patients aged 15-99 years and estimated 1-year age-standardized net survival (ASNS) using the Pohar Perme estimator with 95% confidence intervals (CI). Results Cancer diagnoses showed a minor decline in 2020 compared to previous years. 1-year ASNS remained stable across countries except for significant changes in Australia and the UK. In Australia, lung cancer ASNS increased from 57.2 (95% CI: 55.5-58.9) in 2018 to 62.2 (95% CI: 60.6-63.7) in 2019. Conversely, the UK experienced significant decreases across most cancers; for example, rectum cancer ASNS dropped from 85.3 (95% CI: 84.7-85.9) in 2019 to 83.0 (95% CI: 82.2-83.7) in 2020. These trends were consistent across age, sex, and stage groups, with men and older patients showing lower survival. Discussion Short-term cancer survival remained stable in most high-income countries during the pandemic, showing health system resilience. Due to its larger sample size, the UK may detect year-to-year differences more rapidly than other countries. Long-term studies are needed to assess ongoing impacts. Key messages • Short-term cancer survival remained stable in most high-income countries during the COVID-19 pandemic, indicating health system resilience. • The UK showed significant 1-year survival drops in 2020 for several cancers; large sample size may reveal trends sooner than in other countries.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".