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Record W2919110828 · doi:10.1111/eve.13063

Complex stifle injury in a foal

2019· article· en· W2919110828 on OpenAlexaff
Elizabeth M. Santschi, Jarrod T. Younkin, Christiane Girard, Sheila Laverty

Bibliographic record

VenueEquine Veterinary Education · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversité de Montréal
FundersKansas State University
KeywordsMedicineFoalMedial meniscusLamenessStifle jointAnatomyJoint effusionCruciate ligamentCondyleHorseMeniscusHistopathologySurgeryOsteoarthritisAnterior cruciate ligamentMagnetic resonance imagingRadiologyPathology

Abstract

fetched live from OpenAlex

Summary A 130 kg, 60‐day‐old Quarter Horse male foal presented with bilateral stifle effusion and severe left hindlimb lameness. Clinical examination and imaging including radiography, ultrasound and computed tomography revealed bilateral stifle trauma. Specifically, disruption of the left medial meniscus and deep bone injury to the left medial femoral condyle ( MFC ) were detected, and bilateral injury to the origin of the cranial cruciate ligaments was suspected. Treatment consisted of stall rest and joint injection with corticosteroids, however there was little improvement in lameness. Due to the poor prognosis for soundness, the foal was subject to euthanasia 10 weeks after initial presentation. Post‐mortem examination supported the left medial meniscus and MFC injuries and revealed avulsions of the origin of the cranial cruciate ligaments (complete on the left and partial on the right) from the lateral femoral condyle. Histopathology of the left stifle joint revealed varying depths of MFC osteochondral injury and severe left medial meniscus damage.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.136
GPT teacher head0.451
Teacher spread0.315 · 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 designCase report
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

Citations3
Published2019
Admission routes1
Has abstractyes

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