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Record W2150660638 · doi:10.2217/whe.09.57

Effectiveness of Cervical Cancer Screening at Different Ages

2009· article· en· W2150660638 on OpenAlexaff
Joseph E. Tota, Eduardo L. Franco

Bibliographic record

VenueWomen s Health · 2009
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCervical cancerCervical cancer screeningCervical screeningGynecologyCancerPopulationObstetricsDemographyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.039
GPT teacher head0.398
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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