Malignant transformation of oral submucous fibrosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: This systematic review and meta-analysis aimed to determine the proportion of patients who develop oral carcinomas following a diagnosis of oral submucous fibrosis (OSF) in reported longitudinal studies. We also aimed to evaluate the demographic and clinicopathological factors contributing to the progression of OSF to cancer. METHODS: Individual search strategies were applied for the following bibliographic databases: MEDLINE by PubMed, Scopus, Embase, Web of Science, and Grey literature databases until August 30, 2020. Methodological assessment of the risk of bias of the included studies was undertaken using the modified Newcastle-Ottawa scale. Meta-analyses were conducted using a random-effects (DerSimonian and Liard) method to calculate the pooled proportion of the malignant transformation (MT) in OSF patients. RESULTS: Out of 585 records screened, a total of 9 observational studies were included with a total number of 6,337 patients; of these, 292 OSF cases developed carcinomas. The pooled proportion of the MT was 4.2% (95% CI: 2.7%-5.6%) with an annual transformation rate of 0.73%. Subgroup analysis revealed that the pooled MT proportion was significantly higher among population-based studies in comparison with hospital-based ones (p < .005). Most of the studies showed a high risk of bias. In several studies, there was a lack of information about the demographic and clinicopathological characteristics of OSF patients and associated risk indicators; this insufficiency in details hindered the ability to conduct further subgroup analyses. CONCLUSIONS: Despite the poorly reported and the limited number of studies, our analysis confirms that close to 4% of patients diagnosed with OSF may develop oral cancer. Cases with oral epithelial dysplasia had a higher potential for 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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".