MétaCan
Menu
Back to cohort

Orthodontic Monitoring and Case Finishing With the T-Scan System

2019· book-chapter· en· W2959479247 on OpenAlexaff
Julia Cohen-Lévy

Bibliographic record

VenueAdvances in medical technologies and clinical practice book series · 2019
Typebook-chapter
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOcclusionMedicineOrthodonticsDentistryMalocclusionSurgery

Abstract

fetched live from OpenAlex

This chapter reviews T-Scan use in orthodontics from diagnosis to case finishing, and then in retention, while defining normal T-Scan recording parameters for orthodontically-treated subjects versus untreated subjects. T-Scan use in the case-finishing process is also described, which compensates for changes in the occlusion that occur during “post-orthodontic settling,” as teeth move freely within the periodontium to find an equilibrium position when the orthodontic appliances have been removed. T-Scan implementation is necessary because, often, despite there being a post treatment, visually “perfect” angle's Class I relationship established with the orthodontic treatment, ideal occlusal contacts do not result solely from tooth movement. Creating simultaneous and equal force occlusal contacts following fixed appliance removal can be accomplished using T-Scan data to optimize the end-result occlusal contact pattern. The T-Scan software's force distribution and timing indicators (the two- and three-dimensional force views, force percentage per tooth and arch half, the center of force trajectory and icon, the occlusion time [OT], and the disclusion time [DT]), all aid the Orthodontist in obtaining an ideal occlusal force distribution during case-finishing. Fortunately, most orthodontic cases remain asymptomatic during and after tooth movement. However, an occlusal force imbalance or patient discomfort may occur along with the malocclusion that needs orthodontic treatment. Symptomatic cases require special documentation at the baseline, and careful monitoring throughout the entire orthodontic process. The clinical use of T-Scan in these “fragile” cases of patient muscle in-coordination, mandibular deviation, atypical pain, and/or TMJ idiopathic arthritis, are illustrated by several case reports. The presented clinical examples highlight combining T-Scan data recorded during case diagnosis, tooth movement, and in case finishing, with patients that underwent lingual orthodontics and orthognathic surgery, orthodontic treatment using clear aligners, or conventional fixed treatment with a camouflage treatment plan, which require special occlusal finishing (when premolars are extracted in only one arch).

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)0.001
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.007

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 head0.026
GPT teacher head0.358
Teacher spread0.332 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in medical technologies and clinical practice book seriesSame topicOrthodontics and Dentofacial OrthopedicsFrench-language works237,207