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Record W2585452955 · doi:10.1111/cid.12474

Characteristics of intrabony nerve canals in mandibular interforaminal region by using cone‐beam computed tomography and a recommendation of safe zone for implant and bone harvesting

2017· article· en· W2585452955 on OpenAlexvenueno aff
Xiangwen Yang, F Q Zhang, Yihan Li, Bin Wei, Yao Gong

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2017
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsCone beam computed tomographyChinMandibular canalMandible (arthropod mouthpart)MedicineMental foramenImplantDentistryDental implantInferior alveolar nerveOrthodonticsComputed tomographyRadiographyAnatomyMolarSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Cone-beam computed tomography can accurately show anatomic structure of intrabony nerve canals in mandibular interforaminal region. PURPOSE: The aim was to evaluate the characteristics of intrabony nerve canals in mandibular interforaminal region by using cone-beam computed tomography (CBCT) and determine a safe zone for implant and bone harvesting. MATERIALS AND METHODS: Hemimandibles (824) CBCT images were obtained. The length of the anterior loop (AL), the length and diameter of the mandibular incisive canal (MIC) and its spatial distance in various landmarks were measured. RESULTS: The prevalence of the AL was 93.57%, and the MIC was 97.33%. The mean lengths of the anterior extension of the anterior loop (aAL), caudal extension of the anterior loop (cAL) and the MIC were 2.53 ± 1.27 mm, 6.04 ± 1.66 mm, 9.97 ± 5.15 mm, respectively. The MIC was closer to buccal border and inferior margin of mandible. The length of the AL and diameter of the MIC varied with gender. CONCLUSIONS: The safe zone recommended for implant surgery is 4 mm anterior and 8 mm inferior to the mental foramen, and 10 mm above the inferior margin of mandible. The chin bone should be harvested at least 10 mm below the tooth apices along with a limited depth of 4 mm.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.108
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.422
Teacher spread0.315 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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