Blood Transfusions in Dogs and Cats Receiving Hemodialysis: 230 Cases (June 1997–September 2012)
Bibliographic record
Abstract
BACKGROUND: Multiple factors exist that contribute to anemia in dogs and cats receiving hemodialysis, can necessitate transfusion. OBJECTIVES: To describe blood product usage in dogs and cats with acute and chronic kidney disease that were treated with intermittent hemodialysis to determine risk factors associated with the requirement for blood product transfusion. ANIMALS: 83 cats and 147 dogs undergoing renal replacement therapy at the Animal Medical Center for acute or chronic kidney disease. METHODS: Retrospective medical record review of all dogs and cats receiving renal replacement therapy for kidney disease, from June 1997 through September 2012. RESULTS: Blood products (whole blood, packed RBCs, or stromal-free hemoglobin) were administered to 87% of cats and 32% of dogs. The number of dialysis treatments was associated with the requirement for transfusion in cats (adjusted OR 2.21, 95% CI 1.13, 4.32), but not in dogs (adjusted OR 0.98, 95% CI 0.95, 1.03). Administration of a blood product was associated with a higher likelihood of death in dogs (OR 3.198, 95% CI 1.352, 7.565; P = .0098), but not in cats (OR 1.527, 95% CI 0.5404, 4.317, P = .2). CONCLUSIONS AND CLINICAL IMPORTANCE: Veterinary hospitals with a hemodialysis unit should have reliable and rapid access to safe blood products in order to meet the needs of dogs and cats receiving dialysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".