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Record W7126575755

Aortic Thromboembolism in a Small Breed Dog

2015· other· en· W7126575755 on OpenAlexaboutno aff
Sage De Rosa

Bibliographic record

VenueeCommons (Cornell University) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsParaplegiaBlood pressureLabrador RetrieverChest painPhysical examinationLesionSpinal cordFemoral artery
DOInot available

Abstract

fetched live from OpenAlex

A 7 year-old female spayed Maltese mixed breed dog was referred to the Emergency Service at the Cornell University Hospital for Animals for acute onset paraplegia. The patient had seen the referring veterinarian shortly after clinical signs began and a presumptive diagnosis of intervertebral disc disease was made; the patient was referred to Cornell for further evaluation. On presentation to the Emergency Service, the patient was paraplegic, but deep pain sensation remained intact. Absent femoral pulses were noted bilaterally and the hind paws were cool to the touch when compared to the front paws. Blood pressure was unmeasurable in both pelvic limbs, but was normal in the thoracic limbs. These findings, coupled with a brief, point of care echocardiogram that showed a mass lesion (suspected thrombus) within the left ventricle, were highly suggestive of an aortic thromboembolism. Serial bloodwork, a urinalysis, blood and urine cultures, serial abdominal ultrasounds, an echocardiogram, thoracic radiographs, and a thromboelastogram were supportive of an aortic thromboembolism. The dog was hospitalized for 6 days with slight improvement and discharged on anti-coagulant therapy, broad-spectrum antibiotics, and pain control. Several recheck examinations showed improving motor function, increasingly palpable femoral pulses, as well as dissipation of the presumptive left ventricular thrombus. This report will describe the pertinent clinical findings, diagnostics and treatment in a dog with an aortic thromboembolism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.057
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.021

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 head0.048
GPT teacher head0.215
Teacher spread0.167 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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