American College of Veterinary Emergency and Critical Care (ACVECC) Consensus on the Rational Use of Antithrombotics in Veterinary Critical Care (CURATIVE) guidelines: Small animal
Bibliographic record
Abstract
OBJECTIVES: To systematically review available evidence and establish guidelines related to the risk of developing thrombosis and the management of small animals with antithrombotics. DESIGN: Standardized, systematic evaluation of the literature (identified by searching Medline via PubMed and CAB abstracts) was carried out in 5 domains (Defining populations at risk; Defining rational therapeutic use; Defining evidence-based protocols; Refining and monitoring antithrombotic therapies; and Discontinuing antithrombotic therapies). Evidence evaluation was carried out using Population, Intervention, Comparison, Outcome generated within each domain questions to address specific aims. This was followed by categorization of relevant articles according to level of evidence and quality (Good, Fair, or Poor). Synthesis of these data led to the development of a series of statements. Consensus on the final guidelines was achieved via Delphi-style surveys. Draft recommendations were presented at 2 international veterinary conferences and made available for community assessment, review, and comment prior to final revisions and publication. SETTINGS: Academic and referral veterinary medical centers. RESULTS: Over 500 studies were reviewed in detail. Worksheets from all 5 domains generated 59 statements with 83 guideline recommendations that were refined during 3 rounds of Delphi surveys. A high degree of consensus was reached across all guideline recommendations. CONCLUSIONS: Overall, systematic evidence evaluations yielded more than 80 recommendations for the treatment of small animals with or at risk of developing thrombosis. Numerous significant knowledge gaps were highlighted by the evidence reviews undertaken, indicating the need for substantial additional research in this field.
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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 | 0.113 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".