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Record W4386619568 · doi:10.3390/jcm12185867

Oral Microbiota in Children with Cleft Lip and Palate: A Systematic Review

2023· review· en· W4386619568 on OpenAlexaboutno aff
Jacek Świtała, Magdalena Sycińska-Dziarnowska, Gianrico Spagnuolo, Krzysztof Woźniak, Katarzyna Mańkowska, Liliana Szyszka‐Sommerfeld

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDentistryOrthodontics

Abstract

fetched live from OpenAlex

Background: Cleft in the lip and/or palate (CLP) is a congenital facial deformity that significantly impacts the oral cavity’s structure and function. This malformation can affect the oral microbiota. The objective of this systematic review was to examine and consolidate the current scientific evidence on the oral microflora in children with CLP. Methods: The search strategy included the PubMed, PubMed Central, Web of Science, Scopus, and Embase databases. The inclusion criteria were studies assessing oral microbiota in children with CLP. The Newcastle–Ottawa Scale (NOS) was used to evaluate the quality of the included studies. Results: The search strategy identified 422 potential articles. Twelve papers met the inclusion criteria. High heterogeneity was observed in methodologies, sample sites, and patient characteristics. Eight studies assessed the levels of Streptococcus mutans and Lactobacillus in saliva, with some reporting significantly higher levels in the cleft group compared to controls, while others found no differences. One study reported a significantly higher colonization rate of Candida species in patients with cleft lip and/or palate. Conclusion: The results of the available studies are unclear. Further research is needed to gain a comprehensive understanding of the oral microbiota and potential implications for oral health management in this population. The review was not registered Registration Statement.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.116
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
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 head0.104
GPT teacher head0.472
Teacher spread0.368 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2023
Admission routes1
Has abstractyes

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