RANK, RANKL, and OPG in Dentigerous Cyst, Odontogenic Keratocyst, and Ameloblastoma: A Meta-Analysis
Bibliographic record
Abstract
The aim of this study was to assess and compare RANK, RANKL, and OPG immunoexpression in dentigerous cyst, odontogenic keratocyst, and ameloblastoma. The protocol was registered in PROSPERO (CRD42018105543). Seven databases (Embase, Lilacs, LIVIVO, PubMed, Scopus, SciELO, and Web of Science) were the primary search sources and two databases (Open Grey and Open Thesis) partially captured the "grey literature". Only cross sectional studies were included. The JBI Checklist assessed the risk of bias. A meta-analysis with random effects model estimated the values from the OPG and RANKL ratio reported by the individual studies and respective 95% confidence intervals. The heterogeneity among studies was assessed with I2 statistics. Only nine studies met the inclusion criteria and were considered in the analyses. The studies were published from 2008 to 2018. Two studies presented low risk of bias, while seven studies presented moderate risk. The meta-analysis showed the highest OPG>RANKL ratio for dentigerous cyst (ES=43.3%; 95% CI=14.3-74.8) and odontogenic keratocyst (ES=36.8%; 95% CI=18.8-56.7). In contrast, the highest OPG<RANKL ratio was found for ameloblastoma (ES=73.4%; 95% CI=55.4-88.4) and it was higher in the stromal region compared to the odontogenic epithelial region. The results may explain the aggressive potential of ameloblastoma from the higher OPG
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".