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Record W2996332204

Magnetic resonance imaging muscle lesions in presumptive canine fibrocartilaginous embolic myelopathy.

2018· article· en· W2996332204 on OpenAlexaff
Sabrina M. Martens, Stephanie Nykamp, Fiona James

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineHyperintensityMagnetic resonance imagingMyelopathyNuclear medicineRadiologySpinal cord
DOInot available

Abstract

fetched live from OpenAlex

= 61). It further reports the observation of vertebral column hyperesthesia lasting > 12 hours. The hypothesis tested was that the finding of MRI epaxial muscle hyperintensity correlated with dogs presenting with hyperesthesia. Client-owned dogs diagnosed with presumptive FCEM by specific MRI criteria were included. Statistical analysis was performed using Fisher's exact test. Twenty-three percent (14/61) of MRIs displayed abnormal muscle hyperintensity and 43% (26/61) exhibited vertebral column hyperesthesia. No relationship was found between muscle hyperintensity and pain persisting beyond 12 hours. The muscle hyperintensity remains of unknown significance. That 43% of presumptive FCEM cases have prolonged signs of pain is a higher prevalence than previously reported, and may affect clinical differential diagnoses. This is especially significant in cases in which MRI is not possible and a presumptive diagnosis must be based on the clinical signs.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.041
GPT teacher head0.269
Teacher spread0.228 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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