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Proclination of lower incisors: a design to maximize food penetration and minimize torque

2008· article· en· W1999197174 on OpenAlexaff
Jarin Paphangkorakit, J.W. Osborn

Bibliographic record

VenueJournal of Oral Rehabilitation · 2008
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
FundersKhon Kaen University
KeywordsIncisorSagittal planeOrthodonticsCondyleTorqueMaxillary central incisorDentistryMaterials scienceMathematicsMedicineAnatomyPhysics

Abstract

fetched live from OpenAlex

Human upper and lower incisors are both tilted forward in the sagittal plane. Previous theoretical and in vitro studies have investigated how proclination may help the teeth either to penetrate or to fracture food more effectively or both. We study the proclination of lower incisors in relation to efficiency and to the protection it may offer from potentially damaging torque forces. Lateral cephalographs from 57 normal human subjects were traced. In one study, a line was drawn joining the centre of the condyle to the tip of the lower incisor. The results showed the lower incisor is oriented so that it is closely parallel to the arc of a circle centred at the condyle. In another study, lines were drawn joining the tips of upper and lower incisors at different openings. Each line showed the direction of the force that must be used to bite an object held between the tips of the incisor teeth. Its direction was compared with the direction of the long axis of the lower incisor when the mandible was graphically rotated open. The results showed the long axis of the lower incisor is closest to the direction of the bite force at 12 degrees and 15 degrees of jaw openings (roughly 20-25 mm incisal separation). This is the opening where the maximum incisal force is normally produced. The findings suggest that to reduce the torque, lower incisors implanted or relocated during orthodontic treatment should be oriented parallel to the closing arc.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.297
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2008
Admission routes1
Has abstractyes

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