Role of mast cells in the pathogenesis of oral submucous fibrosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Oral Submucous Fibrosis (OSMF) is an oral potentially malignant disorder (OPMD) that commonly occurs in the South Asian population as there is high usage of areca nut. There has been extensive research on the pathogenesis and treatment of this condition. It is well-established in the scientific literature that mast cells (MC) have a definitive role in several inflammatory disorders. OSMF being a chronic inflammatory disorder, is also expected to have increased MCs. Hence, this review aims to evaluate the role of MCs in the pathogenesis of OSMF. METHODS: A systematic search of articles was performed by two of the authors independently in PubMed, Scopus, Embase, Web of Science, and Google Scholar using the appropriate keywords and Boolean terms. The risk of bias was assessed using the Modified Newcastle-Ottawa Scale. The meta-analysis was performed with R studio software (Version: 4.4.0, Year: 2024, Company: The R foundation for statistical computing). RESULTS: The search retrieved 36 studies, of which 16 were suitable for the review. There is evidence for a marked increase in the number of MCs in OSMF than the normal mucosa upon analyzing the retrieved articles. However, when comparing the grades of OSMF, there are variations in the reports. As all the retrieved articles were case-control studies, the risk of bias was analyzed using the Modified New Castle Ottawa Scale. All the studies scored in the good category (Score 6-9). The pooled effect size shows the Standard Mean Deviation (SMD) to be 0.09, 95% confidence interval (CI) [-0.18;0.37] to lie on either side of no effect. Hence the role of MCs in OSMF has not been established because of homogeneity and consistent sampling error. CONCLUSION: Our systematic review does suggest a definitive role of mast cells in the progression of OSMF. However, there is a lack of methodological rigor in the included studies. This may contribute to diluting the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".