Effectiveness of Cervical Cancer Screening at Different Ages
Bibliographic record
Abstract
Evaluation of: Sasieni P, Castanon A, Cuzick J: Effectiveness of cervical screening with age: population based case-control study of prospectively recorded data. BMJ 339, B2968 (2009). In a large case-control study with cases identified from centers across the UK and using data from the NHS database (collected between 1990 and 2008), investigators evaluated the effectiveness of cervical screening with Pap tests, with a particular focus on women aged 20-24 years. Screening of these young women had little or no effect on the rates of invasive cervical cancer up to 30 years of age. However, screening older women (aged 35-64 years) was shown to reduce cervical cancer risk by approximately 60-80%. This study suggests that the screening age can be safely raised to 25 years without causing harm. Along with the information from this study, policymakers should consider the harms of screening at different ages as well as the absolute rate of cervical cancer in screened and unscreened women of different ages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".