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Comparison of airway dimensions in skeletal Class I malocclusion subjects with different vertical facial patterns

2017· article· en· W2791237323 on OpenAlexaff
Ana Paula Flores-Blancas, Marcos J. Carruitero, Carlos Flores‐Mir

Bibliographic record

VenueDental Press Journal of Orthodontics · 2017
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMalocclusionAnalysis of varianceStudent's t-testMedicineOrthodonticsCephalometryMann–Whitney U testAirwayDentistryMathematicsStatistical significanceInternal medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to compare upper airway widths among skeletal Class I malocclusion subjects with different vertical facial patterns. METHODS: The sample included a total of 99 lateral cephalograms of post pubertal individuals (18.19 ± 1.76 years old). The vertical facial pattern was determined by the Vert index. The McNamara method was used to quantify upper airway widths. ANOVA test and Student's t test for independent groups were used, when normal distribution was not supported Kruskal-Wallis test and U-Mann-Whitney test were used. A multiple linear regression analysis was also performed. RESULTS: Statistically significant differences in several nasopharyngeal widths were found among the distinct vertical facial patterns. Subjects with brachyfacial pattern presented larger nasopharyngeal widths than subjects with mesofacial (p= 0.030) or dolichofacial (p= 0.034) patterns. The larger the Vert value, the larger the nasopharyngeal widths (R2= 26.2%, p< 0.001). At the level of oropharynx no statistically significant differences were found. CONCLUSION: It was concluded that nasopharyngeal linear anteroposterior widths in Class I malocclusion brachyfacial are larger than in mesofacial and dolichofacial individuals. The Vert index only explained 25% of the total variability. No correlation was found for the oropharyngeal widths.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.369
Teacher spread0.317 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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