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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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7260.001

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; both teacher heads agree on what is shown here.

Study designNot applicable
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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