Transfusion practice in dogs and cats: an Internet‐based survey
Bibliographic record
Abstract
OBJECTIVE: To characterize and compare current canine and feline transfusion practices at private referral hospitals (PRH) and veterinary teaching hospitals (VTH), including information regarding blood donor screening; blood product collection, storage, and administration; recipient screening; and monitoring during transfusions. DESIGN: Internet-based survey. SUBJECTS: Sixty-five board-certified specialist veterinarians, 3 veterinarians, and 5 veterinary technicians from 53 PRH and 20 VTH. METHODS: A survey was disseminated via email LIST-SERVs; 1 survey response per hospital was included. MAIN RESULTS: Survey results revealed that PRH more commonly obtained canine and feline blood products solely from blood banks (P < 0.05) and VTH more commonly used hospital-run donor programs (P < 0.05). Canine cryo-poor plasma was more likely to be stored by VTH compared to PRH (P = 0.018) and VTH were more likely to store canine fresh platelet products for >72 hours (P = 0.046). The use of client-owned canine donors (P = 0.043), administration of precollection 1-deamino-8-d-arginine vasopressin to canine donors (P = 0.041), and storage of blood products in a dedicated refrigerator (P = 0.003) and -20°C or -80°C freezer (P = 0.044) were more common in VTH than PRH. However, the use of a refrigerator freezer (P = 0.001), single bag canine collection systems (P = 0.021), and agglutination cards for feline blood typing (P = 0.032), as well as warming of blood products prior to administration (P = 0.021) were more commonly reported by PRH compared to VTH. CONCLUSIONS: Although some transfusion practices including the method and length of storage of blood products, use and screening of blood donors, and administration methods varied between VTH and PRH, most transfusion practices were similar. The information reported from this survey could aid the development of future veterinary transfusion consensus statements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".