Prognostic biomarkers for malignant transformation of oral potentially malignant disorders
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify and systematically map the available evidence concerning the prognostic biomarkers for malignant transformation of oral potentially malignant disorders (OPMDs), and to identify and analyze possible knowledge gaps in this field of literature. INTRODUCTION: It is hypothesized that diagnosis and treatment of oral cancer in its early stages may be the key to improving the prognosis and reducing treatment-related consequences. Oral potentially malignant disorders represent tissue alterations with a higher risk of malignant transformation compared to the normal mucosa. Therefore, the study of prognostic biomarkers for OPMD could represent new diagnosis and therapeutic targets and, consequently, contribute to the reduction of oral cancer burden worldwide. INCLUSION CRITERIA: Longitudinal studies investigating prognostic biomarkers regarding the malignant transformation of OPMD will be included. The initial OPMD diagnosis and the malignant transformation must have been confirmed by histopathological analysis. To achieve minimal heterogeneity, studies that assess biomarkers in other locations (blood, plasma or others) will be excluded. METHODS: Five electronic databases and three grey literature databases will be consulted. No restrictions regarding publication date will be applied. Only studies published in the Latin (Roman) alphabet, which comprises most of the European languages, will be included. Study selection will be performed by two authors in a two-phase process; if any disagreement arises, a third author will be consulted to make a final decision. Data extraction will be performed by two authors using a standardized extraction tool. The results will be described in details accordantly with the aims of this scoping review.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".