Detection of serial changes in absolute myocardial perfusion with 82Rb PET.
Bibliographic record
Abstract
UNLABELLED: Serial changes in myocardial perfusion may represent an important marker of disease progression or regression or the effects of therapy for patients with coronary artery disease (CAD). Quantitative methods have not been developed for the assessment of serial changes in perfusion. The objective of this study was to use receiver operator characteristic (ROC) analysis to determine the sensitivity and specificity of direct paired comparisons (DPCs) to detect changes in absolute myocardial perfusion measured with 82Rb PET. METHODS: Repeated dynamic 82Rb PET scans were obtained on 8 dogs at rest and during hyperemia induced with dobutamine (n = 4) or atrial pacing (n = 4). Radiolabeled microspheres were used to verify perfusion changes. Polar maps of absolute 82Rb retention and associated SD were estimated from the dynamic images. Paired comparisons were then performed using a t test on each of the 532 polar map sectors. Rest-rest and stress-stress differences were used to assess specificity and reproducibility, and stress-rest differences were used to assess sensitivity. RESULTS: 82Rb retention differences of 20% over baseline were detected with 85%-90% sensitivity and specificity, using the optimal DPC probability value and image smoothness. The average 82Rb retention differences correlated well with microspheres (r = 0.74; P = 0.001). Reproducibility of the mean retention values was 4.7% +/- 2.1%. As reproducibility varies, the DPC probability value can be adjusted to maintain specificity. These ROC results are directly applicable to other image modalities that produce measurements with similar SEs (3.7% +/- 0.9%). CONCLUSION: The developed method of DPCs is sensitive and specific for the detection of changes in absolute myocardial perfusion measured with 82Rb PET.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".