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Cephalometric changes in Class II division 1 patients treated with two maxillary premolars extraction

2013· article· en· W2099584606 on OpenAlexaff
Marisana Piano Seben, Fabrício Pinelli Valarelli, Karina Maria Salvatore de Freitas, Rodrigo Hermont Cançado, Aristeu Correa Bittencourt Neto

Bibliographic record

VenueDental Press Journal of Orthodontics · 2013
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOverjetOverbiteMedicineDentistryMalocclusionOrthodonticsCephalometry

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to evaluate the cephalometric alterations in patients with Angle Class II division 1 malocclusion, orthodontically treated with extraction of two maxillary premolars. METHODS: The sample comprised 68 initial and final lateral cephalograms of 34 patients of both sex (mean initial age of 14.03 years and mean final age of 17.25 years), treated with full fixed appliances and extraction of the first maxillary premolars. In order to evaluate the alterations due the treatment between initial and final phases, the dependent t test was applied to the studied cephalometric variables. RESULTS: The dentoskeletal alterations due to extraction of two maxillary premolars in the Class II division 1 malocclusion were: maxillary retrusion, improvement of the maxillomandibular relation, increase of lower anterior facial height, retrusion of the maxillary incisors, buccal inclination, protrusion and extrusion of the mandibular incisors, besides the reduction of overjet and overbite. The tissue alterations showed decrease of the facial convexity and retrusion of the upper lip. CONCLUSIONS: The extraction of two maxillary premolars in Class II division 1 malocclusion promotes dentoskeletal and tissue alterations that contribute to an improvement of the relation between the bone bases and the soft tissue profile.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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.

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

Citations12
Published2013
Admission routes1
Has abstractyes

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