Consensus in Oral Epithelial Dysplasia Classification
Bibliographic record
Abstract
Diagnosis and classification of oral epithelial dysplasia (OED) is critical to identifying and prognosticating patients at risk of squamous cell carcinoma (SCC). However, conventional 3-tiered and 2-tiered grading systems suffer from poor inter-pathologist agreement, and SCC may arise from all grades of OED. This study evaluated pathologist agreement in OED classification as p53 wildtype, p53 abnormal, and HPV-associated based on recent evidence demonstrating the utility of p53/p16 immunohistochemistry (IHC) in this setting and increased risk of p53 abnormal OED progression to SCC, regardless of histologic grade. Fifty digital biopsy specimens were evaluated for diagnosis by 18 subspecialty-trained pathologists, with OED graded utilizing 3-tiered, 2-tiered, and p53 wildtype/p53 abnormal/HPV-associated schemata. Cases were reviewed first without and subsequently with p53/p16 IHC. The cohort consisted of 8 cases of p53 wildtype, 24 cases of p53 abnormal, and 18 cases of HPV-associated OED. Inter-pathologist agreement in OED grading according to 3-tiered (κ=0.32) and 2-tiered (κ=0.39) systems by H&E was poor, but fair-to-good (κ=0.59) in classification as p53 wildtype/p53 abnormal/HPV-associated by H&E and IHC. Classification of OED as p53 wildtype, p53 abnormal, or HPV-associated using p53/p16 IHC outperformed conventional grading in this cohort enriched for p53 abnormal OED, which required correct interpretation of p53 IHC, historically deemed challenging. Routine use of IHC also identifies a wider histologic spectrum of HPV-associated OED than is currently appreciated. More work is needed to determine the efficacy of this classification system in predicting patient outcomes and in guiding management decisions in real-world cohorts.
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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 | 0.045 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".