The in vivo diagnosis of early‐stage aortic valve sclerosis using magnetic resonance imaging in a rabbit model
Bibliographic record
Abstract
PURPOSE: To use magnetic resonance imaging (MRI) to identify and monitor early aortic valve sclerosis (AVS) induced by cholesterol feeding in rabbits. AVS is a highly prevalent disease process, affecting more than 25% of the population over age 65. A major obstacle to early stage medical management of AVS has been the lack of an objective noninvasive technique to identify its presence and monitor its progress. MATERIALS AND METHODS: Retrospectively gated CINE fast spoiled gradient echo (fSPGR) images of aortic valve cusps were collected at 3-month intervals starting at 6 months using a 1.5 T MR scanner interfaced with a customized surface radiofrequency coil. At 16 months a subset of animals was sacrificed and excised cusps were examined with both high frequency ultrasound (US) and histopathological techniques to validate the MRI method. RESULTS: MR and US analysis identified significant thickening of diseased AV cusps when compared to control (P < 0.05). Histopathological analysis confirmed the presence of human-like AVS in diseased rabbit valves. CONCLUSION: Early AVS, exemplified by increased valve thickness, can be identified in vivo using high-resolution MRI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".