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Record W65050931

Human papillomavirus and cervical cancer: burden of illness and basis for prevention.

2006· article· en· W65050931 on OpenAlexaff
Helen Trottier, Eduardo L. Franco

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCervical cancerGenital wartsPapanicolaou stainHPV infectionCancerCervical intraepithelial neoplasiaSex organCervixSubclinical infectionHPV vaccinesGynecologyIntraepithelial neoplasiaObstetricsOncologyInternal medicineProstate cancer
DOInot available

Abstract

fetched live from OpenAlex

Genital infection with human papillomaviruses (HPV) is one of the most common sexually transmitted conditions. The central causal role in cervical carcinogenesis of the so-called high oncogenic-risk (HR)-HPV genotypes, such as HPV-16, has been established as a likely but not sufficient cause of virtually all cases of cervical cancer worldwide. HR-HPV infection also causes a substantial proportion of other anogenital neoplasms and oral squamous cell carcinomas. Infection with low-oncogenic-risk HPV, such as HPV-6 and -11, causes a large proportion of low-grade squamous intraepithelial lesions of the cervix and benign lesions of the anogenital areas known as condylomata acuminata (genital warts). Subclinical and clinical HPV infections are responsible for high morbidity and impose a great burden on the healthcare system. Organized or opportunistic screening with Papanicolaou (Pap) cytology in high-income countries has substantially reduced cervical cancer morbidity and mortality during the last 50 years. However, Pap cytology screening has failed to reduce cervical cancer mortality in many middle-income countries, and most low-income countries cannot make the necessary public health investments to deploy organized screening. The availability of 2 prophylactic HPV vaccines represents the best hope for preventing most cases of cervical cancer and HPV-associated diseases.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.328

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)0.0000.000

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 head0.328
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations84
Published2006
Admission routes1
Has abstractyes

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