Diagnostic Concordance Between Clinical and Histopathologic Diagnoses of Oral Mucosal Lesions: A Retrospective Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".