Effect of Synthetic Colloid Administration on Coagulation in Healthy Dogs and Dogs with Systemic Inflammation
Bibliographic record
Abstract
BACKGROUND: Synthetic colloids are often used during fluid resuscitation and affect coagulation. OBJECTIVE: To compare the effects of an isotonic crystalloid and synthetic colloid on coagulation in healthy dogs and dogs with systemic inflammation. ANIMALS: Sixteen adult purpose-bred Beagles. METHODS: Randomized, placebo-controlled, blinded study. Dogs were randomized into one of two groups receiving fluid resuscitation with either 40 mL/kg IV 0.9% NaCl or tetrastarch after administration of lipopolysaccharide or an equal volume of placebo. After a 14-day washout period, the study was repeated such that dogs received the opposite treatment (LPS or placebo) but the same resuscitation fluid. Blood samples were collected at 0, 1, 2, 4, and 24 hours for measurement of coagulation variables. RESULTS: Administration of either fluid to healthy dogs and dogs with systemic inflammation resulted in similar increases in prothrombin time and activated clotting time. In comparison to saline administration, tetrastarch administration resulted in significantly decreased R (P = .017) in healthy dogs, as well as significantly increased activated partial thromboplastin time (P ≤ .016), CL30% (P ≤ .016), and K (P < .001) and significantly decreased platelet count (P = .019), α (P ≤ .001), MA (P < .001), and von Willebrand factor antigen (P < .001) and collagen binding activity (P ≤ .003) in both healthy dogs and dogs with systemic inflammation. CONCLUSIONS AND CLINICAL IMPORTANCE: Tetrastarch bolus administration to dogs with systemic inflammation resulted in a transient hypocoagulability characterized by a prolonged activated partial thromboplastin time, decreased clot formation speed and clot strength, and acquired type 1 von Willebrand disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".