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Supramandibular Canal Portion Superior to the Fossa of the Submaxillary Gland: A Tomographic Evaluation of the Cross‐Sectional Dimension in the Molar Region

2012· article· en· W2121103716 on OpenAlexvenueno aff
David C. Yu, Bernard Friedland, Nadeem Y. Karimbux, Kevin Guze

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMolarMandibular molarMandibular first molarMandibular second molarImplantDentistryFossaPopulationMandible (arthropod mouthpart)OrthodonticsMedicineAnatomyBiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: Within the fossa of the submaxillary gland (FSG), there is a portion superior to the mandibular canal (SMCP) that can affect implant placement. Our study evaluated this specific portion's prevalence and its average dimensional difference between the first and the second molar regions in a dental implant population. MATERIALS AND METHODS: From 112 patients' mandibular cone beam computerized tomography scans, the SMCPs of the FSG's horizontal and vertical dimensions in the first and second molar positions on both sides were digitally measured. RESULTS: The SMCP of the FSG is larger in the second molar region than in the first molar region in >90% of cases. Average differences were 2.3 mm horizontally and 2.7 mm vertically. Gender difference and intraindividual's left/right variation were both clinically less significant in magnitude than the difference between the molar regions. Taking the 2-mm safety margin above the mandibular canal into consideration, the SMCP of the FSG remained high in prevalence. CONCLUSIONS: The SMCP of the FSG may complicate implant placement more in the second molar region than in the first. Implant planning in the posterior mandibular molar regions should include a SMCP of the FSG evaluation using computer tomography especially in the second molar region.

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)0.013
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 score0.012
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.123
GPT teacher head0.444
Teacher spread0.321 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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