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Record W3100120120 · doi:10.1186/s12903-020-01369-z

Craniofacial morphology/phenotypes influence on mandibular range of movement in the design of a mandibular advancement device

2021· article· en· W3100120120 on OpenAlexaff
M. García Reyes, Alex Bataller Torras, J.A. Cabrera Castillo, Manuel O. Lagravère

Bibliographic record

VenueBMC Oral Health · 2021
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCraniofacialMedicineOrthodonticsMandibular angleMandible (arthropod mouthpart)DentitionMorphology (biology)CondyleDentistryAnatomyMolarBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The mandibular opening path movements have different directions according to the craniofacial morphology of the patient but always downward and backward, therefore increasing the collapse of the upper airway. The aim of this work is to determine if there is a relationship between the craniofacial morphology and the mandibular movement to help understand the impact on the mandibular position. METHODS: 52 students with full permanent dentition aged 19 to 23 years (mean 21.3 SD 1.7; 29 females and 23 males), participated in the study. Each subject had a lateral cephalometric radiograph taken. The opening angle was determined for two levels of vertical openings at 5 and 10 mm. RESULTS: The opening angle showed a greater variability between subjects ranging from 63.15 to 77.08 for 5 mm angle and from for 61.65 to 75.72 for the 10 mm angle. Differences of facial phenotypes was evident when comparing the individual dissoccluding angle of the low angle horizontal pattern and high angle vertical pattern. CONCLUSIONS: The opening angle is related to craniofacial morphology with higher vertical anterior and shorter anteroposterior faces having a more horizontal path of mandibular movement than shorter vertical anterior and longer anteroposterior subjects who have a more vertical path.

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)0.001
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 score0.014
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.352
Teacher spread0.283 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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