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Record W4416801166 · doi:10.7759/cureus.98026

Diagnostic Concordance Between Clinical and Histopathologic Diagnoses of Oral Mucosal Lesions: A Retrospective Study

2025· article· en· W4416801166 on OpenAlexaffabout
Patricia Hernandez-Rivera, Masoud MiriMoghaddam, Hollis Lai, Pallavi Parashar

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConcordanceMedical diagnosisMalignancyBiopsyRetrospective cohort studyHistopathology

Abstract

fetched live from OpenAlex

Introduction Accurate diagnosis is essential for effective treatment and for determining the outcome of any pathologic condition. This study aimed to evaluate the concordance between the clinical and histopathologic diagnoses of biopsied soft-tissue specimens submitted to the Oral Pathology Biopsy Service at the University of Alberta over a 23-year period. Methods The clinical and histopathological diagnoses were coded using the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). Subsequently, all the diagnoses were classified according to their pathogenesis. The outcome measurement was the percentage of absolute concordance, relative concordance, and discordance. Diagnostic agreement was evaluated using Cohen's kappa; sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) were calculated. Additionally, the relationship between gender, age, and pathogenic-cluster concordance was tested using the Chi-square test or the Sample t-test. Results The anonymized database comprised 18,935 oral soft-tissue biopsies. The absolute concordance by SNOMED-CT codes was 47.8%, and by synonyms, 52.1%. The relative concordance was 76.2%, and the discordance was 23.8%. The accuracy of the clinical diagnosis in detecting oral potentially malignant disorders (OPMDs) was evaluated, yielding a sensitivity of 79.3%, a specificity of 97.9%, a PPV of 88.4%, and an NPV of 95.9%. Moreover, for malignant lesions, the clinical diagnosis demonstrated a sensitivity of 67.8%, a specificity of 98.5%, a PPV of 47.0%, and an NPV of 99.4%. Conclusions The results indicated that cluster concordance demonstrated substantial agreement. While clinical examination effectively identifies patients without malignancy or OPMD, it is not sufficiently sensitive to diagnose them. Therefore, histopathologic evaluation of biopsy specimens remains critical for achieving an accurate and definitive diagnosis.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.429
Teacher spread0.360 · 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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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