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Record W4389179909 · doi:10.4103/drj.drj_113_23

Comparison of oral indices in patients with Down syndrome and healthy individuals: A meta-analysis study

2023· article· en· W4389179909 on OpenAlexaboutno aff
Firouzeh Nilchian, Neda Mosayebi, Mohammad Javad Tarrahi, Hamidreza Pasyar

Bibliographic record

VenueDental Research Journal · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisFunnel plotMedicinePublication biasConfidence intervalMEDLINEScopusFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background: The aim of the present study was to compare dental indexes of pediatric Down syndrome (DS) patients to those who are healthy. Materials and Methods: This study was carried out based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. The researchers searched title and abstract of major databases, including ProQuest (ProQuest Dissertations and Theses Full Text: Health and Medicine, ProQuest Nursing and Allie Health Source), PubMed, Google Scholar, clinical key, up to date, springer, Cochrane, Scopus, Embase, and Web of Science (ISI), up to September 2020 with restriction to English and Persian language This meta-analysis study had three outcomes: decay/miss/filled index, plaque index, and gingival index. Effect size, including mean difference and its 95% of confidence interval, was calculated. The Newcastle–Ottawa Scale measured the quality of the selected studies. Heterogeneity was performed using the Q test and I 2 index, and reporting bias was assessed using a funnel plot and Egger and Begg’s tests. Results: Fifteen studies conducted were included in the meta-analysis process. Conclusion: It showed that DS patients had a higher plaque index and gingival index than healthy individuals, which means that the oral health status of these patients is worse and needs more attention.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.223
GPT teacher head0.509
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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