Treatment of Three Cats with Hyperviscosity Syndrome and Congestive Heart Failure Using Plasmapheresis
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".