MétaCan
Menu
Back to cohort
Record W2062331789 · doi:10.1016/j.oooo.2014.02.003

Association of human papilloma virus with atypical and malignant oral papillary lesions

2014· article· en· W2062331789 on OpenAlexaff
Christina McCord, Jing Xu, Wei Xu, Xin Qiu, Nidal Muhanna, Jonathan C. Irish, Iona Leong, R. John McComb, Bayardo Perez‐Ordoñez, Grace Bradley

Bibliographic record

VenueOral Surgery Oral Medicine Oral Pathology and Oral Radiology · 2014
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsMedicinePapillomaImmunohistochemistryPathologyHPV infectionStainingHuman papillomavirusHuman papilloma virusInternal medicineCancerCervical cancer

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to examine atypical and malignant papillary oral lesions for low- and high-risk human papillomavirus (HPV) infection and to correlate HPV infection with clinical and pathologic features. STUDY DESIGN: Sections of 28 atypical papillary lesions (APLs) and 14 malignant papillary lesions (MPLs) were examined for HPV by in situ hybridization and for p16 and MIB-1 by immunohistochemistry; 24 conventional papillomas were studied for comparison. RESULTS: Low-risk HPV was found in 10 of 66 cases, including 9 APLs and 1 papilloma. All low-risk HPV-positive cases showed suprabasilar MIB-1 staining, and the agreement was statistically significant (P < .0001). Diffuse p16 staining combined with high-risk HPV was not seen in any of the cases. A subset of HPV(-) APLs progressed to carcinoma. CONCLUSIONS: Oral papillary lesions are a heterogeneous group. Low-risk HPV infection is associated with a subset of APLs with a benign clinical course. Potentially malignant APLs and MPLs are not associated with low- or high-risk HPV.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueOral Surgery Oral Medicine Oral Pathology and Oral RadiologySame topicHead and Neck Cancer StudiesFrench-language works237,207