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Record W3157374276 · doi:10.21203/rs.3.rs-28100/v2

Effects of mouth breathing on facial skeletal development and malocclusion in children: A systematic review and meta-analysis.

2020· review· en· W3157374276 on OpenAlexaboutno aff
Ziyi Zhao, Leilei Zheng, Xiaoya Huang, Caiyu Li, Liu Jing, Yun Hu

Bibliographic record

VenueResearch Square · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsnot available
FundersProgram for Innovation Team Building at Institutions of Higher Education in ChongqingNational Natural Science Foundation of China
KeywordsMedicineMalocclusionMeta-analysisMouth breathingCochrane LibraryDentistryConfidence intervalMEDLINEBreathingOrthodonticsInternal medicine

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Mouth breathing is closely related to the facial skeletal development and malocclusion. The purpose of this systematic review and meta-analysis was to assess the effect of mouth breathing on facial skeletal development and malocclusion in children. METHODS: An electronic search in PubMed, the Cochrane Library, Medline, Web of Science, EMBASE and Sigle through February 23rd, 2020, was conducted. Inclusion criteria were children under 18 years of age with maxillofacial deformities due to mouth breathing. Methodological quality assessments of the selected articles were performed using the Newcastle-Ottawa Scale. The risk of bias in nonrandomized studies of interventions (ROBINS-I) tool for controlled clinical trials. The Grading of Recommendation, Assessment, Development and Evaluation (GRADE) approach was used for the quality assessment. The included indicators were SNA, SNB, ANB, SN-OP, SNGoGn, MP-H, 1-NA, 1. NA, 1. NB, 1-NB, SPAS, PAS, and C3-H. Data concerning the mean difference in mesial molar movement and extent of canine retraction were extracted for statistical analysis. The mean differences and 95% confidence intervals were analyzed for continuous data. Review Manager 5.3, was used to synthesize various parameters associated with the impact of mouth breathing on facial skeletal development and malocclusion. RESULTS: Following full-text evaluations for eligibility, 6 studies were included in the final quantitative synthesis. In Sagittal direction, SNA (MD: -1.61, P <0.0001), SNB (MD: -1.99, P <0.0001), ANB (MD: 0.95, P = 0.0005),1-NA (MD: 0.72, P = 0.004), 1. NA (MD: 1.98, P = 0.020), 1-NB (MD: 1.06, P < 0.0001), In vertical direction, SN-OP (MD: 3.20, P < 0.0001), SNGoGn (MD: 4.34, P < 0.0001). In airway, SPAS (MD: -5.23, P < 0.0001), PAS (MD: -2.11, P < 0.0001), and C3-H (MD: -1.34, P < 0.0001). CONCLUSIONS: The results showed that the mandible and maxilla rotated backward and downward, and the occlusal plane was steep. In addition, mouth breathing presented a tendency of labial inclination of the upper anterior teeth. Airway stenosis was comm on in mouth-breathing children. TRIAL REGISTRATION: crd-register@york.ac.uk; registration number CRD42019129198

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)0.013
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.029
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.393
Teacher spread0.337 · 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
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

Citations1
Published2020
Admission routes1
Has abstractyes

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