2021 ISFM Consensus Guidelines on the Collection and Administration of Blood and Blood Products in Cats
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Blood and blood products are increasingly available for practitioners to use in the management of haematological conditions, and can be lifesaving and therapeutically useful for patients with anaemia and/or coagulopathies. It is important for feline healthcare that donors are selected appropriately, and transfusions of blood or blood products are given to recipients that will benefit from them. Complications can occur, but can be largely avoided with careful donor management and recipient selection, understanding of blood type compatibility, and transfusion monitoring. CLINICAL CHALLENGES: Feline blood transfusion, while potentially a lifesaving procedure, can also be detrimental to donor and recipient without precautions. Cats have naturally occurring alloantibodies to red cell antigens and severe reactions can occur with type-mismatched transfusions. Blood transfusions can also transmit infectious agents to the recipient, so donor testing is essential. Finally, donors must be in good health, and sedated as appropriate, with blood collected in a safe and sterile fashion to optimise the benefit to recipients. Transfusion reactions are possible and can be mild to severe in nature. Autologous blood transfusions and xenotransfusions may be considered in certain situations. EVIDENCE BASE: These Guidelines have been created by a panel of authors convened by the International Society of Feline Medicine (ISFM), based on available literature. They are aimed at general practitioners to provide a practical guide to blood typing, cross-matching, and blood collection and administration.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".