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Record W4399992360 · doi:10.1016/j.xaor.2024.06.003

Protocol for facially guided digital orthodontic diagnosis and treatment planning

2024· article· en· W4399992360 on OpenAlexaff
Rupert HG Kelley, Carlos Flores‐Mir, Jorge Ayala Puente, Álvaro Ferrando Cascales, Itamar Michael Friedländer, Raúl Ferrando Cascales

Bibliographic record

VenueAJO-DO Clinical Companion · 2024
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSuperimpositionWorkflowProtocol (science)Multidisciplinary approachComputer scienceRadiation treatment planningPlan (archaeology)Medical physicsMedicineArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

Using a digital workflow in orthodontics and interdisciplinary dentistry undoubtedly benefits both the patient and professional. This 9-step protocol guides orthodontists through an interdisciplinary treatment planning and execution. First, it establishes an exhaustive digital diagnosis and then formulates an interdisciplinary digital treatment plan with 3-dimensional (3D) goals. Finally, objective verification of the treatment results occurs through 3D superimposition. This 9-step protocol proposes a fully integrated workflow platform by providing a digital, 3D, clear planning protocol for the interdisciplinary team. It gives them a clear vision of successfully managing the patient from start to finish. It is versatile, logical, and methodical and can be applied to any clinical situation involving orthodontics.

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)0.012
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0740.025

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 head0.296
GPT teacher head0.522
Teacher spread0.226 · 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
GenreProtocol

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

Citations2
Published2024
Admission routes1
Has abstractyes

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