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Record W2403483328 · doi:10.1097/olq.0000000000000319

Associations of Anogenital Low-Risk Human Papillomavirus Infection With Cancer and Acquisition of HIV

2015· review· en· W2403483328 on OpenAlexaff
Liga Bennetts, Monika Wagner, Anna R. Giuliano, Joel M. Palefsky, Marc Steben, Thomas Weiß

Bibliographic record

VenueSexually Transmitted Diseases · 2015
Typereview
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsMedicineHPV infectionCervical cancerHuman papillomavirusAnal cancerRisk factorCancerOncologyPapillomaviridaeBovine papillomavirusImmunologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

α-Mucosal human papillomavirus (HPV) types are implicated in a range of clinical conditions and categorized as "low-risk" (LR) and "high-risk" (HR) types according to their degree of association with cervical cancers. The causative role of LR HPV infection in the development of anogenital warts and in low-grade squamous intraepithelial lesions is well established. In addition, there is a growing body of evidence that infection with LR HPV types may be associated with an elevated risk of cancers and potentiation of coinfections. Prospective and case-control studies consistently report a higher risk of anogenital cancers in men and women with a history of anogenital warts. Based on currently available evidence, this higher risk may be due to shared exposure to HR HPV types or an underlying immune impairment, rather than a direct role of LR HPV types in subsequent cancer risk. Data also suggest that infection with LR HPV, HR HPV, or both may increase the risk of HIV acquisition, although the relative contribution of different HPV types is not yet known. There is also evidence implicating HPV clearance, rather than HPV infection, in increased risk of HIV acquisition.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.381
Teacher spread0.331 · 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 designOther design
Domainnot available
GenreReview

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

Citations15
Published2015
Admission routes1
Has abstractyes

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