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Record W2901317686 · doi:10.2319/031918-218.1

Non-invasive evaluation of periodontal ligament stiffness during orthodontic tooth movement

2018· article· en· W2901317686 on OpenAlexaff
Lindsey Westover, Gary Faulkner, Carlos Flores‐Mir, William Hodgetts, Don Raboud

Bibliographic record

VenueThe Angle Orthodontist · 2018
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
Fundersnot available
KeywordsPeriodontal fiberMedicineDentistryStiffnessOrthodonticsMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the longitudinal changes in periodontal ligament (PDL) stiffness during orthodontic tooth movement using the Advanced System for Implant Stability Testing (ASIST). MATERIALS AND METHODS: ASIST measurements of maxillary canines that were actively retracted into an extraction space were collected approximately once per month for 12 adolescent female patients. The ASIST Stability Coefficient (ASC) values, which are directly related to PDL stiffness, were determined for each visit to examine longitudinal changes for individual canines as they were exposed to different forces (approximately 80 and 150 g) during retraction. RESULTS: The pattern of longitudinal changes in ASC was similar for both canines (regardless of the two force levels applied) in individual patients and across patients. All patients showed some decrease in ASC, with an average maximum reduction in stiffness of 73.4 ± 7.7%. Some recovery was observed for most patients; however, none of the patients had the PDL stiffness return to the pre-treatment value at the final measurement appointment which was some time close after space closure was completed. On average, the ASC value at the final measured visit was 48.1 ± 12.2% of the initial value. No measurements are available after removal of orthodontic appliances and during retention. CONCLUSIONS: The ASIST was able to detect changes in PDL stiffness during orthodontic treatment, providing some insight into the mechanical changes that occur at the tooth root interface.

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)0.003
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 score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.309
Teacher spread0.272 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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