MétaCan
Menu
Back to cohort
Record W2766201089 · doi:10.3415/vcot-17-02-0019

Variation in the Quantity of Elastic Fibres with Degeneration in Canine Cranial Cruciate Ligaments from Labrador Retrievers

2017· article· en· W2766201089 on OpenAlexaboutno aff
Kei Hayashi, Dylan N. Clements, Peter Clegg, John Innes, Eithne Comerford

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilUniversity of Liverpool
KeywordsCruciate ligamentMedicineAnatomyStifle jointLigamentBreedSkullStainAnterior cruciate ligamentPathologyStainingBiology

Abstract

fetched live from OpenAlex

Abstract Objectives This study aims to quantify numbers of elastic fibres in cranial cruciate ligaments from a dog breed at high risk of cranial cruciate ligament disease. Methods Macroscopically normal cranial cruciate ligaments were harvested from six Labrador retrievers. Sequential histological sections were assessed for extracellular matrix degeneration (haematoxylin and eosin stain) and elastic fibre staining (Miller’s stain). Elastic fibres were semi-quantified using previously published scoring systems. Each section was scored twice by two observers. Results Increased numbers of elastic fibres were seen with increasing cranial cruciate ligament degeneration (p = 0.001). Labrador retriever cranial cruciate ligaments had lower elastic fibre staining when compared with previous published findings in the racing greyhound. Clinical Significance The cranial cruciate ligaments from a dog breed at high risk of cranial cruciate ligament disease vary in the quantity of elastic fibres in association with ligament degeneration. Breed variation in the quantity of elastic fibres may reflect differing risk of cranial cruciate ligament disease.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.125
GPT teacher head0.337
Teacher spread0.213 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary and Comparative Orthopaedics and TraumatologySame topicVeterinary Orthopedics and NeurologyFrench-language works237,207