Experimental aortic regurgitation in rats under echocardiographic guidance.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Experimental aortic regurgitation (AR) induced by retrograde perforation of the aortic leaflets has been used as an animal model of volume overload, but causes high mortality. In the past, AR was induced under hemodynamic guidance, but echocardiography can be used to guide the investigator as well as grade AR severity and monitor left ventricular function. The study aim was to assess the value of echocardiography in experimental AR in rats. METHODS: Sixty-six Wistar rats (bodyweight 250-275 g) underwent perforation of the aortic leaflets via a right transcarotid approach to induce moderate to severe AR. Transthoracic echocardiographic guidance was used to assess catheter location, degree of AR, ventricular function and complications. RESULTS: Echocardiographic images were easily obtained and of excellent quality (M-mode, two-dimensional and complete Doppler evaluation). Catheter location, movement and guidance during aortic valve perforation were straightforward, and AR gradation and ventricular function easily assessed. Procedural complications and causes of death were identified. Had hemodynamic criteria been used, 24% of AR cases would have been misgraded. With echocardiography, the overall mortality rate was 17% (<10% in the last 25 animals). CONCLUSION: Echocardiographic guidance is mandatory for protocols of experimental AR. Using this technique, the procedure is simplified, and is more accurate, more reproducible and safer for the animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".