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Record W4379229339 · doi:10.3390/oral3020020

Bilateral Condylar Fracture: A 10-Year Case Report Follow-Up after a Conservative Myofunctional Approach

2023· article· en· W4379229339 on OpenAlexaff
Antonio Bedoya-Rodríguez, German O Ramirez-Yañez

Bibliographic record

VenueOral · 2023
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsNorth York General Hospital
Fundersnot available
KeywordsCondyleMedicineConservative treatmentDentistryOrthodonticsConservative managementSurgery

Abstract

fetched live from OpenAlex

Mandibular condylar fractures can be approached with invasive techniques, such as surgical repositioning and fixation of the fractured condyle, or with conservative techniques using myofunctional appliances. Recent publications recommend non-invasive or conservative approaches when treating mandibular condylar fractures, as they may produce more stable results and fewer consequences. However, to the knowledge of the authors, there is no long-term follow up publications of clinical cases treated with a conservative approach. This report presents a ten-year follow-up of a case report published two years ago by the same authors. In the previous report, the authors showed an eleven year-old girl treated with a myofunctional appliance due to a bilateral mandibular condylar fracture. The treatment produced a stable result over the following ten years with the patient not using any appliance after the myofunctional treatment was completed. The benefits of a conservative treatment, as well as the consequences of mandibular condylar fracture, such as a heart shaped mandibular condyle, are discussed here by the authors.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0040.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.290
Teacher spread0.250 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

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