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MR IMAGING FINDINGS IN A DOG WITH LUMBAR GANGLION CYSTS

2001· article· en· W1983029527 on OpenAlexaff
Aubrey A. Webb, John W. Pharr, Linda J. Lew, Kimberly A. Tryon

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2001
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineMagnetic resonance imagingSacrumHistopathologyRadiographyVertebraSyringomyeliaRadiologyLumbarAnatomyPathology

Abstract

fetched live from OpenAlex

Intraspinal cysts of the L6-L7 and L7-S1 articular process joints in a six-year-old neutered female German Shepherd Dog were diagnosed using magnetic resonance (MR) imaging. Histopathology provided a diagnosis of ganglion cysts. Clinical, laboratory, radiographic and MR imaging findings are described. Briefly, radiographic findings revealed lumbarization of the first sacral vertebra, and fusion of the first caudal vertebra to the sacrum. In addition, spondylosis and articular process osteoarthrosis occurred at L6-L7 and L7-S1. MR imaging revealed multiple, well encapsulated structures ranging in size from 3-10 mm in diameter which were found to arise from the L6-L7 and L7-S1 articular process joints. These cysts had signal intensities that varied from hyperintense to the cerebrospinal fluid (CSF) on T1 weighted images to isointense to CSF on T2 weighted images. Decompressive surgery in conjunction with arthrodesis of these joints resulted in resolution of clinical signs. The dog remained pain-free 1 1/2 years following surgical therapy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.295
Teacher spread0.265 · 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 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

Citations38
Published2001
Admission routes1
Has abstractyes

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