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Record W1949514370 · doi:10.1002/cncr.29264

Trends in cervical intraepithelial neoplasia Grade 2+ after human papillomavirus vaccination: The devil is in the details

2015· letter· en· W1949514370 on OpenAlexaff
Harinder Brar, Allan Covens

Bibliographic record

VenueCancer · 2015
Typeletter
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCervical cancerVaccinationCervical intraepithelial neoplasiaDysplasiaIncidence (geometry)Genital wartsCohortGynecologyCervical screeningIntraepithelial neoplasiaCytologyCancerObstetricsInternal medicineImmunologyPathology

Abstract

fetched live from OpenAlex

Eight years since the introduction of the adolescent HPV vaccination programs, as the vaccinated cohort enters adulthood, the impact of these vaccination programs is now being assessed through surrogate markers such as the incidence of high‐grade cervical dysplasia (cytology/histology) and the incidence of genital warts. Early data from the HPV‐IMPACT study shows that although there has been a noticeable drop in CIN2+ incidence rates, the results may be confounded by the recent changes in cervical screening guidelines. This study is significant in that it is the first to report on changes in high‐grade histological abnormalities in the postvaccination era. Despite the confounding effect of changing cervical cancer screening guidelines, the findings cannot be completely ignored. With wider acceptance and standardization of cervical cancer screening guidelines and with an increasing number of women entering the vaccinated cohort, the magnitude of vaccine effectiveness should become more obvious. Further studies are needed to assess the changes in high‐grade histological abnormalities in the post‐vaccine era.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.071
GPT teacher head0.384
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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

Citations0
Published2015
Admission routes1
Has abstractyes

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