Clinical Features Associated With Malignant Transformation of Low‐Grade Dysplasia
Bibliographic record
Abstract
BACKGROUND: Inferring risk for malignant transformation (MT) in patients with lesions diagnosed as mild or moderate oral epithelial dysplasia (low-grade OED) remains challenging. We developed two models assessing the risk of progression to high-grade OED (severe dysplasia or carcinoma in situ) or OSCC in patients with low-grade OED lesions. METHODS: We included demographic, risk habit and clinical data from participants with low-grade OED lesions enrolled in the BC Oral Cancer Prevention Program's Oral Cancer Prediction Longitudinal study. Cox proportional hazard models were fit to estimate the effects of anatomic site and toluidine blue findings and adjusted for confounders, as both are associated with MT in the literature but without a North American-specific cohort analysis. Our primary model included both variables of interest. A secondary model included only anatomic site since toluidine blue is not in widespread use. RESULTS: Five hundred and thirty-four participants with 605 lesions met final inclusion criteria, with 339 mild and 266 moderate OED at baseline. In the primary model, lesions at a high-risk anatomic site or with positive toluidine blue staining were associated with a 2.6 and 2.4-fold increased risk of progression, respectively. In the second model that did not incorporate toluidine blue, high-risk anatomic site remained a highly associated risk factor (2.7-fold increased risk of progression). CONCLUSION: Lesion anatomic site is associated with higher risk of MT for the general practitioner, while a specialist with access to toluidine blue results can assume additional risk associated with positive staining. These models may inform decisions for surveillance and intervention for OED.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".