Exploring the role of angiogenesis in fibrosis and malignant transformation in oral submucous fibrosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Angiogenesis is a crucial molecular driver of fibrosis in various inflammatory lesions. Oral submucous fibrosis (OSMF) is a chronic inflammatory fibrotic disorder with malignant potential. The angiogenetic pathways in OSMF remain obscure due to limited research, necessitating an in-depth review. This review aimed to illuminate the cryptic pathogenetic mechanisms of angiogenesis in the disease progression/fibrosis of OSMF and its malignant transformation, providing insights for improved treatment. Extensive literature searches were conducted across an array of databases until October 2023. Original research articles on angiogenesis in OSMF were included, and the risk of bias was assessed using the modified Newcastle-Ottawa scale. RevMan ver. 5.4 (Cochrane Collaboration) was used for data analysis. Thirty-four articles were included for qualitative synthesis and seven for quantitative analysis. Findings revealed that angiogenesis was significantly increased in early-stage OSMF but decreased as the disease advanced. It was also associated with the severity of epithelial dysplasia and malignant transformation. A random-effects model confirmed the upregulation of angiogenesis as a significant risk factor in early-stage fibrosis and malignant transformation. The mounting evidence reinforces that angiogenesis plays a crucial role in the progression of early-stage fibrosis of OSMF and its malignant transformation, opening avenues for diagnostic and therapeutic interventions.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".