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Record W7117556221 · doi:10.1002/hed.70148

Biopsy for Suspicious Oral Lesions: A Review From the American Head and Neck Society‐Cancer Prevention Service

2025· article· en· W7117556221 on OpenAlexaff
James Christopher Gates, Heather Edwards, Nick Purdy, Michael Troka, Peter Varela, Quinn Self, Yingci Liu, Yusuf Dündar, Patricia Joyce Brooks, Dauren Adilbay, Andrew C. Birkeland, John D. Cramer

Bibliographic record

VenueHead & Neck · 2025
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiopsyHead and neckService (business)MEDLINEHead (geology)

Abstract

fetched live from OpenAlex

BACKGROUND: Oral cancer is often preceded by a precursor lesion. This presents an opportunity for early diagnosis and intervention. Method of biopsy and interpretation are not well standardized and novel methods of analysis are now being investigated. METHODS: We conducted a narrative review of PubMed/MEDLINE (last search August 31, 2025), focusing on adult oral precancerous lesions evaluated in outpatient settings. RESULTS: Incisional punch biopsy is reproducible and often provides the diagnostic information needed. However, scalpel biopsy should be considered when initial biopsy is equivocal, depth of invasion is desired, or to minimize sampling bias. Limited studies show improved sensitivity of combining saliva and plasma sampling. Targeted fluorescent imaging may aid in future biopsy site selection. AI has shown encouraging results in both automated detection of dysplasia and prediction of malignant progression, achieving performance comparable to clinically validated grading systems. CONCLUSION: This update serves to further inform biopsy of oral suspicious lesions and provide a framework for future investigation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.424
Teacher spread0.366 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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