Deep Vein Thrombosis Induced by Stasis in Mice Monitored by High Frequency Ultrasonography
Bibliographic record
Abstract
Venous thrombosis is a common condition affecting 1 - 2% of the population, with an annual incidence of 1 in 500. Venous thrombosis can lead to death through pulmonary embolism or results in the post-thrombotic syndrome, characterized by chronic leg pain, swelling, and ulceration, or in chronic pulmonary hypertension resulting in significant chronic respiratory compromise. This is the most common cardiovascular disease after myocardial infarction and ischemic stroke and is a clinical challenge for all medical disciplines, as it can complicate the course of other disorders such as cancer, systemic disease, surgery, and major trauma. Experimental models are necessary to study these mechanisms. The stasis model induces consistent thrombus size and a quantifiable amount of thrombus. However, it is necessary to systematically ligate side branches of the inferior vena cava to avoid variability in thrombus sizes and any erroneous data interpretation. We have developed a non-invasive technique to measure thrombus size using ultrasonography. Using this technique, we can assess thrombus development and resolution over time in the same animal. This approach limits the number of mice required for quantification of venous thrombosis consistent with the principle of replacement, reduction, and refinement of animals in research. We have demonstrated that thrombus weight and histological analysis of thrombus size correlate with measurement obtained with ultrasonography. Therefore, the current study describes how to induce deep vein thrombosis in mice using the inferior vena cava stasis model and how to monitor it using high frequency ultrasound.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".