Comparison of arterial and venous whole blood clot initiation, formation, and strength by thromboelastography in anesthetized swine
Bibliographic record
Abstract
Thromboelastography (TEG) analysis was used to determine if differences exist between venous and arterial samples in anesthetized swine, using identical sampling techniques for each of the samples. We hypothesized that TEG parameters would not differ between native whole blood venous and arterial samples. Thirty male Landrace swines were included in the study. Both the femoral artery and vein were catheterized using standard cut-down techniques and with identically sized catheters to rule out any catheter size effects on the results. Standard TEG parameters for native whole venous and arterial blood samples (r, K, α, MA, G, and coagulation index) were measured or calculated, and t-test or Mann-Whitney rank-sum test used for comparison when appropriate. Significant differences were detected for r (venous < arterial), K (venous < arterial), α (venous > arterial), and coagulation index (venous > arterial) TEG parameters. No significant differences were measured for MA or G. These differences are important, especially when temporal changes in TEG are utilized to monitor patient stability and fluid therapy protocols using trends in coagulation properties. Taken together, these results suggest that clots are more likely to form at a faster rate in venous samples compared to arterial samples, but the overall clot strength does not differ. Therefore, if TEG analysis is being used to monitor coagulation profiles in a patient, care should be taken to use the same site and technique if results are to be used for comparative purposes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".