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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 OpenAlexaff
Julien Olive, Tim Mair, B. Majoie Charles

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations48
Published2009
Admission routes1
Has abstractyes

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