Cervical screening during the COVID-19 pandemic: optimising recovery strategies
Bibliographic record
Abstract
Disruptions to cancer screening services have been experienced in most settings as a consequence of the COVID-19 pandemic. Ideally, programmes would resolve backlogs by temporarily expanding capacity; however, in practice, this is often not possible. We aim to inform the deliberations of decision makers in high-income settings regarding their cervical cancer screening policy response. We caution against performance measures that rely solely on restoring testing volumes to pre-pandemic levels because they will be less effective at mitigating excess cancer diagnoses than will targeted measures. These measures might exacerbate pre-existing inequalities in accessing cervical screening by disregarding the risk profile of the individuals attending. Modelling of cervical screening outcomes before and during the pandemic supports risk-based strategies as the most effective way for screening services to recover. The degree to which screening is organised will determine the feasibility of deploying some risk-based strategies, but implementation of age-based risk stratification should be universally feasible.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".