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Record W2075139305 · doi:10.1159/000214921

Detection and Typing of Human Papillomavirus Nucleic Acids in Biological Fluids

2009· review· en· W2075139305 on OpenAlexafffund
François Coutlée, Marie‐Hélène Mayrand, Michel Roger, Eduardo L. Franco

Bibliographic record

VenuePublic Health Genomics · 2009
Typereview
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsTypingHPV infectionHuman papillomavirusNucleic acidMedicineCervical cancerPapillomaviridaeVirologyCancerBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Human papillomaviruses (HPV) are the etiologic agents of cancer of the uterine cervix and several other neoplasias. Detection of HPV infection will improve the sensitivity of primary and secondary screening of cervical cancer. The clinical indications for the use of HPV tests will have to consider the natural history of HPV infection and diseases, and the multiplicity of types involved. Signal amplification HPV DNA tests detect several high-risk HPV types, are standardized, commercially available and approved for clinical use. Nucleic acid amplification techniques are ideal methods for epidemiologic purposes since they minimize misclassification of HPV infection status and allow detection of infection with low viral burden. They are currently under evaluation for clinical use. PCR is the most widespread method for HPV typing, especially with the use of consensus primers and typing with reverse hybridization techniques. Novel promising HPV detection strategies are now proposed, such as HPV mRNA detection, and suspension or solid phase arrays. These novel techniques will have to be evaluated as stringently as actual assays in clinical studies. Although assays have been developed for the evaluation of viral load, viral integration and HPV polymorphism in molecular epidemiological studies, their role in clinical practice is not currently defined.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.245
GPT teacher head0.448
Teacher spread0.203 · 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 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

Citations22
Published2009
Admission routes2
Has abstractyes

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