Participation in the nationwide cervical cancer screening programme in Denmark during the COVID-19 pandemic: An observational study
Bibliographic record
Abstract
Background: In contrast to most of the world, the cervical cancer screening programme continued in Denmark throughout the COVID-19 pandemic. We examined the cervical cancer screening participation during the pandemic in Denmark. Methods: We included all women aged 23-64 y old invited to participate in cervical cancer screening from 2015 to 2021 as registered in the Cervical Cancer Screening Database combined with population-wide registries. Using a generalised linear model, we estimated prevalence ratios (PRs) and 95% CIs of cervical cancer screening participation within 90, 180, and 365 d since invitation during the pandemic in comparison with the previous years adjusting for age, year, and month of invitation. Results: Altogether, 2,220,000 invited women (in 1,466,353 individuals) were included in the study. Before the pandemic, 36% of invited women participated in screening within 90 d, 54% participated within 180 d, and 65% participated within 365 d. At the start of the pandemic, participation in cervical cancer screening within 90 d was lower (pre-lockdown PR = 0.58; 95% CI: 0.56-0.59 and first lockdown PR = 0.76; 95% CI: 0.75-0.77) compared with the previous years. A reduction in participation within 180 d was also seen during pre-lockdown (PR = 0.89; 95% CI: 0.88-0.90) and first lockdown (PR = 0.92; 95% CI: 0.91-0.93). Allowing for 365 d to participation, only a slight reduction (3%) in participation was seen with slightly lower participation in some groups (immigrants, low education, and low income). Conclusions: The overall participation in cervical cancer screening was reduced during the early phase of the pandemic. However, the decline almost diminished with longer follow-up time. Funding: The study was funded by the Danish Cancer Society Scientific Committee (grant number R321-A17417) and the Danish regions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".