Global impact of COVID-19 on organized CRC screening programs: lessons learned
Bibliographic record
Abstract
Using a standardized data template, this study retrospectively collected data about colorectal cancer (CRC) screening activity in 2020 and 2021 to estimate the impact of the COVID-19 pandemic compared to the pre-pandemic period (2018 or 2019). Data were collected from 17 programs in 14 countries of which 15 were population-based programs. Invitation coverage was decreased by up to 53.7 % in 2020. Participation among those invited was similar in both periods for all programs. The maximum backlog in invitations was less than 7.4 months in 2020 and 3.3 months for 2021. Nine out of 15 programs observed a decrease in the number of detected CRCs in 2020. Four programs showed a positive percentage change in CRCs detected in 2021 relative to the pre-pandemic period. Half of the countries observed a worse stage-distribution in 2020/2021. Overall, organized CRC screening programs operated at lower screening activity, but screening outcomes were similar compared to the pre-pandemic period.
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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 | 0.043 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".