Quantitative gated PET for the assessment of left ventricular function in small animals.
Bibliographic record
Abstract
UNLABELLED: 18F-FDG PET can identify areas of myocardial viability and necrosis and provide useful information on the effectiveness of experimental techniques designed to improve contractile function and myocardial vascularization in small animals. The left ventricular volume (LVV) and left ventricular ejection fraction (LVEF) in normal and diseased rats were measured in vivo using the high-resolution avalanche photodiode (APD) small-animal PET scanner of the Université de Sherbrooke. The measurements obtained by PET were compared with those obtained by high-resolution echocardiography and with known values obtained from a small, variable-volume cardiac phantom. METHODS: List-mode gated (18)F-FDG PET studies were performed using the APD PET scanner on 30 rats: 11 healthy, 4 under septic shock, and 15 with heart failure induced by ligature of the left coronary artery. PET images were resized to match human-scale pixels and analyzed using a standard clinical cardiac software program. The LVV and LVEF from the same animals were also evaluated by echocardiography. RESULTS: Agreement was excellent between the endocardial volumes determined by PET and the actual volumes of the cardiac phantom (r(2) = 0.96). Agreement between PET and echocardiography for LVV ranged from good in healthy rats (r(2) = 0.89) to fair in diseased rats (r(2) = 0.49). Agreement was fair between LVEF values measured by the 2 methods (r(2) = 0.56). Normal rats had an average LVEF of 83.2% +/- 8.0% using PET and 81.6% +/- 6.0% using echocardiography. In rats with heart failure, LVEF was 54.6% +/- 15.9% using PET and 54.2% +/- 13.3% using echocardiography. CONCLUSION: Both PET and echocardiography clearly differentiated normal rats from rats with heart failure. Echocardiography is fast and convenient, whereas list-mode PET is also able to assess defect size, myocardial viability, and metabolism.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".