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Record W4210821639

Guidelines for Orthodontic Evaluation and Preparation for Orthognathic Surgery Patients.

2022· article· en· W4210821639 on OpenAlexaff
Miguel Hirschhaut, Carlos Flores‐Mir

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOrthognathic surgeryMedicineDentofacial DeformitySagittal planeOrthodonticsDentistryMalocclusion
DOInot available

Abstract

fetched live from OpenAlex

Well-managed orthodontic therapy is vital to the success of oral and maxillofacial surgery, helping to facilitate optimal skeletal correction along with ideal dentofacial esthetics and long-term occlusal function. In the treatment of skeletally mature patients with dentofacial deformities, orthodontics often needs to be combined with oral and maxillofacial surgery to correct transverse, vertical, and sagittal skeletal discrepancies and facial asymmetries that affect function and facial esthetics. Supported by published literature, this article presents a detailed discussion of key considerations for the management of orthodontic/orthognathic surgery cases. Step-by-step recommendations encompassing case diagnosis, presurgical preparation, and postsurgical management are provided.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0460.027

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 head0.165
GPT teacher head0.367
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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