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Record W2339305378 · doi:10.1002/ar.23349

The blood supply to the canine middle ear

2016· article· en· W2339305378 on OpenAlexfundno aff
Cathryn Stevens‐Sparks, Keith Jarrett, Margaret A. McNulty, George M. Strain

Bibliographic record

VenueThe Anatomical Record · 2016
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsnot available
FundersCanadian Blood ServicesSchool of Veterinary Medicine, Louisiana State UniversityUniversity of Utah
KeywordsBlood supplyMiddle earAudiologyMedicineBusinessAnatomySurgery

Abstract

fetched live from OpenAlex

Current descriptions of the anatomy of the blood supply to the canine middle ear are either incomplete or inconsistent, particularly in regards to the vascular branches in close proximity to the temporomandibular articulation (TMJ). To further investigate this blood supply, dissections (n = 9), corrosion casts (n = 4), and computed tomography (n = 8) of canine temporal regions/ears were performed. The goal of this study was to identify and describe branches of the external carotid and maxillary arteries in close proximity to the TMJ that supply the middle ear of the dog. Specific focus was placed on the constancy and origin of the canine rostral tympanic artery since this artery was anticipated to arise from the maxillary artery and enter a foramen at the medial aspect of the mandibular fossa adjacent to the TMJ. New anatomical variations of three canine arteries are described in this study. (1) The rostral tympanic artery is a branch of the temporomandibular ramus and is accommodated by a small foramen located within a depression medial to the temporomandibular joint. (2) A pharyngeal branch of the caudal deep temporal artery was identified. (3) The origin of the caudal auricular artery occurred opposite the lingual artery in 25.8% of dissected specimens, contrary to published descriptions. Anat Rec, 299:907-917, 2016. © 2016 Wiley Periodicals, Inc.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.001

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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 designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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