Differential Expression of Immunohistochemical Markers in Ameloblastoma & Ameloblastic Carcinoma: A Systematic Review and Meta-analysis of observational studies
Bibliographic record
Abstract
Background: Differentiating between ameloblastoma (AB) and ameloblastic carcinoma (AC) is difficult, especially when AB has atypical cytological characteristics or an uncommon clinical history. This systematic review and meta-analysis aimed to elucidate the differential expression of immunohistochemical markers between AB and AC. Methods: We conducted a thorough search of PUBMED and SCOPUS according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify cross-sectional studies that compared the expression of immunohistochemical markers in AB and AC. We used a random-effects model to analyze the risk ratios and their corresponding 95% confidence intervals (CIs). The quality of the included studies was assessed using the Newcastle-Ottawa scale. The Egger's test was used to assess publication bias. Results: In total, 301 articles were identified. After excluding irrelevant titles and abstracts, 86 articles were selected for full-text review. We categorized the 41 markers into proliferative and non-proliferative markers. Among non-proliferative markers, nuclear markers were differentially expressed in AB and AC. SOX2 was the only marker that significantly differentiated AB and AC, with an RR of -0.19 (CI 0.10-0.36, I2=0). Conclusion: The current evidence suggests the significance of SOX2 in differentiating between AB and AC, warranting prospective confirmation in well-defined extensive studies. We highlight the paucity of high-quality replicated studies of other markers in this field. Collaborative efforts with standardized techniques are necessary to generate clinically useful immunohistochemical markers.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".