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Record W7126654003

Splenic Mass in a Geriatric Mixed Breed Dog

2018· other· en· W7126654003 on OpenAlexaboutno aff
MJ Sun

Bibliographic record

VenueeCommons (Cornell University) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSplenectomyHistopathologySplenic diseaseSpleenAnemiaHemangiosarcomaHematomaLabrador RetrieverPhysical examinationAbdominal mass
DOInot available

Abstract

fetched live from OpenAlex

An 11-year-old male castrated mixed breed dog presented to the primary veterinarian (rDVM) for lethargy, brown urine, and pale mucous membrane before he was referred to Cornell University Hospital for Animals for splenectomy. Physical examination of this patient revealed pertinent abnormalities including mildly pale mucous membranes and a palpable abdominal mass. A complete blood count (CBC) and serum chemistry revealed abnormalities consistent with the patient's previously diagnosed hypothyroidism, but no other clinically significant abnormalities. An abdominal ultrasound revealed a large irregular splenic mass. A splenectomy was performed. The whole spleen and a nodule on the omentum were submitted for histopathology. Even though hemangiosarcoma (HSA) was highly suspected, the histopathology result was consistent with a splenic hematoma and omental daughter spleens. The dog was clinically doing well three months after the surgery, but his blood work at the rDVM showed mild anemia and hypoalbuminemia. No further diagnostics were pursued at this point. Using this case as a framework, the characteristic presentation of splenic HSA, common diagnostic findings, and long-term outcome of patients with splenic hematoma after splenectomy are reviewed.

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 score0.046
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
Bibliometrics0.0070.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.066

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 head0.024
GPT teacher head0.195
Teacher spread0.171 · 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
Published2018
Admission routes1
Has abstractyes

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Same venueeCommons (Cornell University)French-language works237,207