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Record W2915082671 · doi:10.2460/ajvr.80.3.235

Ex vivo computed tomography evaluation of loading position on morphometry of the caudal cervical intervertebral disk spaces of dogs

2019· article· en· W2915082671 on OpenAlexaff
Sebastian Knell, Lucas A. Smolders, Thomas Steffen, Antonio Pozzi

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2019
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnatomyCervical vertebraeWedge (geometry)Intervertebral discDorsumCervical spineComputed tomographyMedicineBiomedical engineeringMaterials scienceRadiologySurgeryGeometryMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE To provide an objective, quantitative morphometric description of the caudal cervical intervertebral disk (IVD) spaces of dogs. SAMPLE Vertebral specimens consisting of C4 through C7 from 5 medium-sized dogs. PROCEDURES CT images were obtained with the specimens positioned in neutral, flexion, extension, and lateral bending positions. Size and shape of the cranial and caudal end plates, angle between the end plates (IVD wedge angle), and craniocaudal distance (IVD width) between end plates for the 4 loading positions were measured and compared for the 3 segments (C4-5, C5-6, and C6-7). RESULTS End plate size and shape, IVD wedge angle, and IVD width were not significantly different among the 3 segments. Caudal cervical end plates were consistently larger than cranial cervical end plates. The IVD wedge angle ranged from -4.8° to 15.2°. Flexion induced a reduction in IVD width in the ventral portion of the IVD, whereas extension induced a decrease in width in the dorsal portion of the IVD. Central IVD width remained unchanged among the loading positions. CONCLUSIONS AND CLINICAL RELEVANCE Unique morphometric and dynamic characteristics of the caudal cervical IVD space of dogs were detected. These findings may help investigators when designing IVD prostheses for dogs with cervical spondylomyelopathy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.093
GPT teacher head0.417
Teacher spread0.323 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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