MétaCan
Menu
Back to cohort
Record W2328681655 · doi:10.4161/hv.28742

Human papillomaviruses-related cancers

2014· review· en· W2328681655 on OpenAlexafffund
Ala‐Eddin Al Moustafa, Rana Al‐Awadhi, Nabiha Missaoui, Ishag Adam, Raika Durusoy, Lina Ghabreau, Nizar Akil, Hussain Gadelkarim Ahmed, Amber Yasmeen, Ghazi Alsbeih

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2014
Typereview
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsConcordia UniversityMcGill University
FundersCanadian Institutes of Health ResearchCancer Research Society
KeywordsMedicineCervical cancerHuman papillomavirusCancerVaccinationEpidemiologyHPV vaccinesHPV infectionImmunologyVirologyInternal medicine

Abstract

fetched live from OpenAlex

Human papillomavirus (HPV) infections are estimated to be the most common sexually transmitted infections worldwide. Meanwhile, it is well established that infection by high-risk HPVs is considered the major cause of cervical cancer since more than 96% of these cancers are positive for high-risk HPVs, especially types 16 and 18. Moreover, during the last 2 decades, numerous studies pointed-out the possible involvement of high-risk HPV in several human carcinomas including head and neck, colorectal and breast cancers. The association between high-risk HPVs and cervical cancer and potentially other human malignancies would necessitate the introduction of vaccines which were generated against the 2 most frequent high-risk HPVs (types 16 and 18) worldwide, including the Middle East (ME) as well as North African countries. The presence of high-risk HPVs in the pathogenesis of human cancers in the ME, which is essential in order to evaluate the importance of vaccination against HPVs, has not been fully investigated yet. In this review, we present an overview of the existing epidemiological evidence regarding the presence of HPV in human cancers in the ME and the potential impact of vaccination against HPV infections and its outcome on human health in this region.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.110
GPT teacher head0.444
Teacher spread0.334 · 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 designNot applicable
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

Citations41
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueHuman Vaccines & ImmunotherapeuticsSame topicCervical Cancer and HPV ResearchFrench-language works237,207