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Record W1958926663 · doi:10.1111/cid.12365

The Occurrence Rate and Diameter of Arteries Traveling Near the Mandible and an Assessment of the Relative Hemorrhage Risk in Implant Surgery

2015· article· en· W1958926663 on OpenAlexvenueno aff
Yuji Katsumi, Ritsuo Takagi, Hayato Ohshima

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2015
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMolarPremolarImplantIncisorDentistryMandible (arthropod mouthpart)Maxillary arteryCadaverArteryAnatomyOrthodonticsSurgeryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Bleeding in the floor of the mouth during implant surgery is attributed to arterial injuries in the sublingual space. PURPOSE: This study aimed to assess the relative hemorrhage risk during implant surgery. MATERIALS AND METHODS: We investigated the occurrence rate and diameter of submental and sublingual arteries with special reference to their relationship with the course patterns of these arteries using 26 human cadavers. RESULTS: Three types of arteries were distinguished: main duct (MD), mucoperiosteal branches (MB), and cortical branches (CB). The occurrence rate of MB and CB was significantly high at the central incisor region in the upper part of the mylohyoid muscle, whereas the diameter of the MB and CB was significantly smaller than the occurrence rate of MD at the incisor regions. The occurrence rate of MD in the submental artery was significantly higher at the lateral incisor, canine, and premolar regions in the lower parts, whereas the occurrence rate of MD was significantly lower at the second and third molars in the upper parts. CONCLUSION: The susceptibility of the submental artery to injury is suggested at the incisors, canine, premolars, and first molar regions during implant surgery.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.122
GPT teacher head0.457
Teacher spread0.335 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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