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Record W2765113356 · doi:10.1186/s40634-017-0109-1

Determination of knee cartilage volume and surface area in beagle dogs: a pilot study

2017· article· en· W2765113356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Experimental Orthopaedics · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsAurinia (Canada)Fowler Kennedy Sport Medicine ClinicWestern University
FundersEupraxia Pharmaceuticals
KeywordsBeagleCartilageKnee cartilageOsteoarthritisMedicineAnatomyVolume (thermodynamics)Labrador RetrieverPathologyArticular cartilageInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background The objective of this study was to determine the cartilage volume and surface area of male and female Beagle dog knees using 3D (3 dimensional) reconstructed MRI images. Methods Six Beagle Dogs (Canis familiaris) (3 males and 3 females) of 10‐18 months old and weighing between 7.2 and 17.1 kg underwent a MRI evaluation of both knees. The data acquired allowed a 3D reconstruction of the knee and measurement of the cartilage volume and surface area. Results Mean knee cartilage volume (averaged over the right and left knees) of animals between 7.2 and 17.1 kg ranged from 319.7 to 647.3 mm 3 ; while the mean knee cartilage surface area ranged from 427.14 to 757.2 mm 2 . There was evidence of both knee volume and surface area increasing linearly with animal bodyweight. Conclusions The cartilage volume and surface area of the Beagle dog appears to correlate significantly with body weight. This study provides a reference base for future studies investigating cartilage related pathology such as osteoarthritis.

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.

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)0.000
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 score0.296
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.080
GPT teacher head0.352
Teacher spread0.271 · 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