Prothrombotic and Inflammatory Effects of Intravenous Administration of Human Immunoglobulin G in Dogs
Bibliographic record
Abstract
BACKGROUND: Intravenous administration of human immunoglobulin G (hIVIgG) has been suggested to potentiate thromboembolism in dogs, but supportive scientific reports are lacking. OBJECTIVES: To determine if hIVIgG therapy promotes hypercoagulability and inflammation in dogs. ANIMALS: Twelve healthy Beagle dogs. METHODS: Prospective, experimental trial. An hIVIgG/saline solution was infused IV at 1 g/kg BW over 8 hours to 6 dogs, and physiological saline was infused to the other 6 dogs. Blood samples were drawn before, during, and after infusion for serial measurement of indicators of coagulation and inflammation. Data were analyzed by 2-way repeated measures analysis of variance. RESULTS: Dogs administered hIVIgG developed mildly decreased blood platelet concentrations without thrombocytopenia (median, 200 x 10(3)/microL; range, 150-302 x 10(3)/microL; P < .01), leukopenia (median, 3.5 x 10(3)/microL; range, 20-62 x 10(3)/microL; P < .001), and mildly increased plasma total protein concentrations (median, 6.3 g/dL; range, 5.6-6.7 g/dL; P < .001). Administration of hIVIgG was also associated with increases in fibrin/fibrinogen degradation products in all dogs (either 5 microg/mL or 10 microg/dL), thrombin-antithrombin III complexes (median, 7.2 ng/mL; range, 4.9-14.2 ng/mL; P < .001), and C-reactive protein concentrations (median, 2.5 mg/dL; range, 0.5-4.3 mg/dL; P < .01). CONCLUSION AND CLINICAL IMPORTANCE: Administration of hIVIgG to dogs promotes hypercoagulability and an inflammatory state. This should be further evaluated and considered when using hIVIgG in dogs with IMHA or other prothrombotic conditions.
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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) | 0.000 | 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".