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Clinical and Cephalometric Correlation between Mouth-breathing and Nasal-breathing Children

2025· article· en· W4412916949 on OpenAlexaff
Prıyanka Balakrıshnan, Daya Srinivasan, A. Senthil, Kavita Arunagiri

Bibliographic record

VenueInternational Journal of Clinical Pediatric Dentistry · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineMouth breathingBreathingElectronic journalDentistryOrthodonticsCorrelationAnesthesiaFree access

Abstract

fetched live from OpenAlex

Introduction: Pediatric sleep-disordered breathing encompasses conditions from upper airway resistance syndrome to obstructive sleep apnea. Mouth-breathing in children causes severe dentoalveolar deformities. Adenotonsillar hypertrophy exacerbates mouth-breathing, greatly influencing dentofacial development. Early detection of mouth-breathing habit is crucial to prevent the development of malocclusion. Mouth-breathing also alters the salivary pH of the saliva and has an impact on gingival health. Aim: The study aims to compare and correlate the clinical and cephalometric parameters between nasal-breathing children (NBC) and mouth-breathing children (MBC) among the age-group of 6-12 years. Materials and methods: A cross-sectional study was conducted to analyze the clinical and cephalometric variables between NBC and MBC among the age-group of 6-12 years who reported to the Department of Pediatric and Preventive Dentistry with the chief complaint of malocclusion. Sixty-six children were assessed for the breathing pattern and categorized into NBC and MBC based on the clinical history and assessment test. After initial screening and examination, the children were referred to the Department of ENT for otolaryngology assessment. Both the groups were assessed for clinical parameters such as malocclusion, salivary pH, tonsillar hypertrophy, and gingival inflammation. Lateral cephalometric assessment was done for both the groups and all the values were tabulated. All the data were analyzed statistically using Statistical Package for the Social Sciences (SPSS) software version 20. Results: = 0.001)] compared to NBC. Conclusion: MBC show increased prevalence of malocclusion, tonsillar hypertrophy, gingival inflammation, and decreased saliva pH, impacting dentofacial development, which needs early intervention. How to cite this article: Balakrishnan P, Srinivasan D, Senthil ARE et al. Clinical and Cephalometric Correlation between Mouth-breathing and Nasal-breathing Children. Int J Clin Pediatr Dent 2025;18(5):514-521.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.134
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.444
Teacher spread0.399 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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