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Record W2104494932 · doi:10.2746/095777309x383612

Use of standing low‐field magnetic resonance imaging to diagnose middle phalanx bone marrow lesions in horses

2009· article· en· W2104494932 on OpenAlex
Julien Olive, Tim Mair, B. Majoie Charles

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.

Bibliographic record

VenueEquine Veterinary Education · 2009
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineMagnetic resonance imagingLamenessPhalanxOsteoarthritisInterphalangeal JointBone marrowOccultSoft tissueRadiologyPathologyAnatomy

Abstract

fetched live from OpenAlex

Summary Bone marrow lesions (BMLs) (also known as ‘bone bruises’, ‘bone oedema’, ‘bone contusions’ and ‘occult fractures’) within the middle phalanx were diagnosed by standing low field magnetic resonance imaging (MRI) in 7 horses. The lesions were characterised by low signal intensity on T1‐ and T2*‐weighted gradient echo sequences, mildly increased signal intensity on T2 fast spin echo sequences, and high signal intensity on short tau inversion recovery (STIR) sequences. Four distinct patterns of abnormal signal were identified: BML associated with osteoarthritis of either the proximal or distal interphalangeal joints; BML associated with soft tissue injury; BML associated with acute trauma; and BML unassociated with any other injury or lameness (assumed to represent bone response to biomechanical stress). Repeat MRI was undertaken in 4 cases. In most cases the BML resolved with rest and time, although lameness was persistent in 2 horses (one of which had an associated osteoarthritis of the proximal interphalangeal joint).

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.577

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 head0.046
GPT teacher head0.281
Teacher spread0.235 · 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