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Record W4285745177 · doi:10.3389/fonc.2022.864820

Molecular Detection Methods in HPV-Related Cancers

2022· review· en· W4285745177 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImmunohistochemistryPolymerase chain reactionHead and neck squamous-cell carcinomaMedicineDigital polymerase chain reactionBiomarkerIn situ hybridizationHead and neck cancerHuman papillomavirusCancerPathologyCervical cancerOncologyCancer researchInternal medicineBiologyGeneMessenger RNA

Abstract

fetched live from OpenAlex

Human papillomavirus (HPV) is responsible for most cervical cancers and some head and neck cancers, including oropharyngeal squamous cell carcinoma and sinonasal carcinoma. Cervical cancer is commonly diagnosed by liquid-based cytology, followed by HPV testing using commercially available DNA polymerase chain reaction (PCR), p16 immunohistochemistry (IHC), or DNA/RNA in situ hybridization. HPV in head and neck cancers is commonly diagnosed by p16 IHC or by RT-qPCR of HPV-16 E6 and E7 oncoproteins. Droplet digital PCR has been reported as an ultrasensitive and highly precise method of nucleic acid quantification for biomarker analysis and has been used to detect oncogenic HPV in oropharyngeal and cervical cancers.

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.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.057
GPT teacher head0.449
Teacher spread0.392 · 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