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Record W1963985609 · doi:10.5326/jaaha-ms-5635

Treatment of Three Cats with Hyperviscosity Syndrome and Congestive Heart Failure Using Plasmapheresis

2010· article· en· W1963985609 on OpenAlexaff
Tonya E. Boyle, Marie K. Holowaychuk, Allison K. Adams, Steven L. Marks

Bibliographic record

VenueJournal of the American Animal Hospital Association · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPlasmapheresisMedicineCATSHeart failureHyperviscosity syndromeTachypneaHyperviscosityCardiologyInternal medicineAnesthesiaMultiple myelomaTachycardiaBlood viscosityImmunology

Abstract

fetched live from OpenAlex

Three cats were evaluated at a veterinary teaching hospital for congestive heart failure (CHF) secondary to hyperviscosity syndrome from plasma cell neoplasia. All cats had severe hyperproteinemia due to hyperglobulinemia. Multiple myeloma or plasma cell neoplasia was diagnosed based on cytopathology and post mortem examination. The cats presented with signs of CHF including acute collapse, tachypnea, increased respiratory effort, and pulmonary crackles. All cats had heart murmurs and echocardiographic signs consistent with hypertrophic cardiomyopathy. An enlarged left atrium was found in all cats and two of three cats also had spontaneous echocardiographic contrast. Plasmapheresis (centrifugal plasma exchange) was performed on all three cats by the removal of whole blood and the infusion of a balanced electrolyte solution while the whole blood was centrifuged and separated. The RBCs were then washed before being readministered to the patient. Plasmapheresis alleviated the clinical signs of CHF (tachypnea) in all three cats. Plasmapheresis should be considered in cases of CHF secondary to hyperviscosity syndrome to rapidly alleviate clinical signs associated with heart failure while diagnosis of the underlying cause is made and appropriate therapy implemented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.230
Teacher spread0.223 · 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.

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

Citations24
Published2010
Admission routes1
Has abstractyes

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