Unveiling mechanistic effects of mast cell in the progression of fibrosis and malignant transformation of oral submucous fibrosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Oral submucous fibrosis (OSMF) is a ubiquitous fatal fibrotic mucosal disease with multifactorial etiology and complex pathogenesis. The role of mast cells in the pathophysiology of OSMF remains uncharted territory owing to a dearth of studies. Thus, the present systematic review and meta-analysis aimed to unentangle the mysteric role of mast cells in the pathogenesis, progression of fibrosis and malignant transformation of OSMF. Using various databases, full-text articles that investigated mast cell concentrations in OSMF were entailed for review. A modified Newcastle-Ottawa scale was employed to evaluate the risk of bias in all articles and Review Manager was utilized for meta-analysis. Twenty and fourteen qualified articles, respectively, were included for qualitative and quantitative data synthesis. Progressive amplification of mast cell density is linked with fibrosis-induced malignant transformation of OSMF. The fixed-effect model also confirmed that significantly upregulated mast cell counts have a decreased risk of association with control as well as a significantly increased risk of being associated with early-stage fibrosis and malignant transformation of OSMF. This review authenticates the mechanistic effects of mast cells in the pathogenesis, chronicity, progression of fibrosis and malignant transformation of OSMF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".