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Record W4394424225 · doi:10.6084/m9.figshare.20003114

RANK, RANKL, and OPG in Dentigerous Cyst, Odontogenic Keratocyst, and Ameloblastoma: A Meta-Analysis

2022· dataset· en· W4394424225 on OpenAlexaff
Igor Felipe Pereira Lima, Felipe Rodrigues de Matos, Ítalo de Macedo Bernardino, Ingrede Tatiane Serafim Santana, Walbert de Andrade Vieira, Cauane Blumenberg, Walter L. Siqueira, Luiz Renato Paranhos

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsKeratocystAmeloblastomaDentigerous cystOdontogenicOdontogenic cystRank (graph theory)MedicineDentistryPathologyMathematicsMolar

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.051
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.072
GPT teacher head0.303
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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