S100A7 as a predictive biomarker in malignant transformation of oral epithelial dysplastic lesions
Bibliographic record
Abstract
BACKGROUND: S100A7 expression is increased in oral potentially malignant disorders (OPMD) at risk of transformation to oral squamous cell carcinoma (OSCC). The objective of this study was to evaluate S100A7 expression in OPMD which transformed and to correlate these results with the 3-tier and 2-tier dysplasia grading systems, and an S100A7 immunohistochemistry-based signature algorithm (S100A7 ARS). METHODS: Formalin fixed paraffin embedded specimens from 48 patients with OPMD that had transformed into OSCC were selected. Thirty-five patients with multiple biopsies of dysplasia which had not transformed, and 25 cases with normal appearing and/or hyperkeratotic oral mucosa were included as control groups. Specimens were stained for S100A7 protein by immunohistochemical methods. Expression of S100A7 was assessed semi-quantitatively and by image analysis for the S100A7 ARS. RESULTS: The semi-quantitative score had strong correlation with the S100A7 ARS and allowed differentiation of OPMD from the Control groups. The S100A7ARS was also useful in differentiation of OPMD that transformed to carcinoma from non-transforming cases (p < 0.05). CONCLUSION: S100A7 immunohistochemical staining and the S100A7 ARS has potential for identifying oral potentially malignant lesions that have an increased risk of malignant transformation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".