Association of Veterinary Hematology and Transfusion Medicine (AVHTM) transfusion reaction small animal consensus statement (TRACS). Part 3: Diagnosis and treatment
Bibliographic record
Abstract
OBJECTIVE: To systematically review available evidence to develop guidelines for diagnosis and treatment of transfusion-associated reactions in dogs and cats. DESIGN: Standardized and systemic evaluation of the literature (identified through Medline via PubMed and Google Scholar searches) was carried out for identified transfusion reaction types in dogs and cats. The available evidence was evaluated using PICO (Population, Intervention, Comparison, Outcome) questions generated for each reaction type. The evidence was categorized by level of evidence (LOE) and quality (Good, Fair, or Poor). Guidelines, diagnostic, and treatment algorithms were generated based on the evaluation of the evidence. Consensus on the final guidelines was achieved through Delphi-style surveys. Draft recommendations were disseminated through veterinary specialty listservs for review and comments, which were evaluated and integrated prior to final publication. RESULTS: Medline via PubMed and Google Scholar databases were searched. There were 14 Population Intervention Comparison Outcome questions identified and corresponding worksheets were developed focusing on the diagnosis and treatment of transfusion-associated reactions in dogs and cats. Fourteen guidelines and four algorithms were developed with a high degree of consensus. CONCLUSIONS: This systematic evidence evaluation process yielded recommended diagnostic and treatment algorithms for use in practice. However, significant knowledge gaps were identified, demonstrating the need for additional research in veterinary transfusion medicine.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".