Clinicopathological Parameters Related to Malignant Transformation of Oral Leukoplakia: A Meta-Analysis
Bibliographic record
Abstract
Objective. To assess the clinical-pathological factors related to the malignant transformation of oral leukoplakia. Materials and Methods. A search for articles on malignant transformation factors related to oral leukoplakia was conducted in the following electronic databases: PubMed (MEDLINE, Cochrane Library), Web of Science (WoS) and Google Scholar. Thirty-seven articles with a low-moderate risk of bias according to the Newcastle-Ottawa methodological quality scale were included in this meta-analysis. The data were analyzed using the statistical programs RevMan 5.4 (The Cochrane Collaboration, Oxford, UK) and MedCalc Statistical Software version 16.4.3 (MedCalc Software Ltd. Ostend, Belgium) programs. The estimated prevalence was calculated according to DerSimonian and Laird random method. For dichotomous outcomes, the estimates of effects of an intervention were expressed as odds ratios (OR) using the Mantel-Haenszel (M-H) method with 95% confidence intervals. Results. The estimated global prevalence of malignant transformation of oral leukoplakia was 9.15%. The factors with the highest malignant transformation risk of oral leukoplakia were: non-homogeneous clinical types (OR: 5.41; p<0.001); leukoplakias with moderate-severe dysplasia (OR: 3.43; p<0.001); lesions located on the tongue and/or the floor of the mouth (OR: 3.19; p<0.001); leukoplakias in non-smokers (OR: 2.08; p<0.001) and lesions in women (OR: 1.73; p<0.001). In contrast, older age or regular alcohol intake were factors without significant influence (p>0.05).Conclusions. Non-homogenous oral leukoplakias and with moderate-severe dysplasia are those with the highest probability 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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".