The impact of episodic screening interruption: COVID-19 and population-based cancer screening in Canada
Bibliographic record
Abstract
BACKGROUND: Population-based cancer screening can reduce cancer burden but was interrupted temporarily due to the COVID-19 pandemic. We estimated the long-term clinical impact of breast and colorectal cancer screening interruptions in Canada using a validated mathematical model. METHODS: We used the OncoSim breast and colorectal cancers microsimulation models to explore scenarios of primary screening stops for 3, 6, and 12 months followed by 6-24-month transition periods of reduced screening volumes. For breast cancer, we estimated changes in cancer incidence over time, additional advanced-stage cases diagnosed, and excess cancer deaths in 2020-2029. For colorectal cancer, we estimated changes in cancer incidence over time, undiagnosed advanced adenomas and colorectal cancers in 2020, and lifetime excess cancer incidence and deaths. RESULTS: Our simulations projected a surge of cancer cases when screening resumes. For breast cancer screening, a three-month interruption could increase cases diagnosed at advanced stages (310 more) and cancer deaths (110 more) in 2020-2029. A six-month interruption could lead to 670 extra advanced cancers and 250 additional cancer deaths. For colorectal cancers, a six-month suspension of primary screening could increase cancer incidence by 2200 cases with 960 more cancer deaths over the lifetime. Longer interruptions, and reduced volumes when screening resumes, would further increase excess cancer deaths. CONCLUSIONS: Interruptions in cancer screening will lead to additional cancer deaths, additional advanced cancers diagnosed, and a surge in demand for downstream resources when screening resumes. An effective strategy is needed to minimize potential harm to people who missed their screening.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".