Bibliographic record
Abstract
Abstract Abstract 5061 Venous thromboembolism afflicts 117 people per 100,000 each year and is an important cause of morbidity and mortality. There has been extensive research dedicated to the clinical aspect of venous thrombosis, especially with regards to its diagnosis and treatment. However, animal models studying this phenomenon are scarce and, in most cases, very crude. Developing a murine model of venous thrombosis using techniques similar to the ones used to detect thrombosis in humans can be a constructive step in studying this phenomenon in more detail. The model developed in our lab uses ultrasound imaging to visualize venous clots in the Inferior Vena Cava (IVC) of mice, allowing for precise measurements of the formed clot. Ligation of the IVC is one of the well established models for studying thrombosis in mice. We ligated the IVC of wild type C57B6 mice, and allowed them to recover. We then followed clot formation at several time points after the operation using micro-ultrasonography, the Vevo 770®, a novel imaging ultrasound technology designed to monitor murine vasculature. To assess the precision of the clot measurements, we then sacrificed the mice, and dissected out the thrombi in order to precisely measure and weigh them. A thrombosis develops only after 5 hours of ligation post surgery when a clot is visualized in the IVC. The clot increases slightly over the next 24 hours. The measurements of the clot after dissection correlates favourably with the measurements done by ultrasonagraphy using the Vevo770®. These data suggest that the Vevo770® can be used as a reliable technique for non-invasive assessment of venous thromboembolism in mice. Developing a murine model for thrombosis using more accurate, and clinically more relevant techniques such as ultrasonography, is a step towards better understanding and treatment of venous thromboembolism. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".