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Record W4403157986 · doi:10.1111/vru.13444

Ultrasonographic assessment of equine metacarpal cartilage thickness is more accurate than computed tomographic arthrography

2024· article· en· W4403157986 on OpenAlexafffund
Sèamus Hoey, Ursula Fogarty, Hester McAllister, Antonella Puggioni, Brian Cloak, Hélène Richard, Cliona Skelly, Sheila Laverty

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2024
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversité de Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineSagittal planeComputed tomographicOsteochondrosisCartilageHistologyRadiologyUltrasonographyAnatomyNuclear medicineComputed tomographyPathology

Abstract

fetched live from OpenAlex

Articular cartilage can be directly imaged using ultrasonography. The fetlock is a common site of osteochondrosis, with the sagittal ridge of the third metacarpal bone most commonly affected. In osteochondrosis, cartilage thickening may be an initial finding. This postmortem study investigated the ability of ultrasonography to accurately measure the dorsodistal articular cartilage of the third metacarpal bone in young horses, compared to computed tomographic arthrography (CTA) and histological measurements. A total of 33 metacarpophalangeal joints from 18 horses between the ages of 12 days and 10 months old were imaged ultrasonographically and with CTA and sectioned and measured using histology. Imaging measurements were made by two observers. Despite overall weak agreement between ultrasonography and histology, the best agreement was at the distal aspect of the sagittal ridge of the third metacarpal bone. Interobserver agreement at this site was also moderate. CTA showed poor agreement overall with histology. Cartilage thickness decreased with age on ultrasonography, CTA, and histology. In conclusion, ultrasonography is a more accurate imaging modality than CTA in the assessment of cartilage in young horses.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.403
Teacher spread0.328 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes2
Has abstractyes

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