Commentary on “Cervical cancer screening in England: The past, present, and future”: A comparison with Canada
Bibliographic record
Abstract
For cervical cancer screening to be most effective, all eligible women in the population must be enrolled in an organized program. In this issue, Albrow et al describe how cervical cancer screening in England migrated from "opportunistic" in the 1960s to "organized" in the 1980s and discuss its continued evolution as new screening approaches and technologies developed. England's current National Health Services Cervical Screening Programme is the poster child for organized screening delivery. A similar model of central coordination and peripheral delivery of screening does not exist in Canada. The provision of health services is under provincial jurisdiction, and consequently each province administers its own version of a cervical cancer screening program. To date, organized screening has only been achieved in 2 of 10 provinces, and screening elsewhere is opportunistic. Screening in Canada begins at age 18-21, ends at age 69-70, and mostly occurs annually. This approach has yielded a significant reduction in cervical cancer incidence and mortality, but health service utilization costs have been high. The future should see full implementation of organized screening in all provinces, and when combined with the publicly funded human papillomavirus vaccination program of young girls begun in 2008, the result should be even better cancer control. Cancer (Cancer Cytopathol) 2012;. © 2011 American Cancer Society.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.031 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".