Quantification of the normal range of myocardial blood flow and flow reserve with <sup>82</sup>rubidium versus <sup>13</sup>N-ammonia PET
Bibliographic record
Abstract
Coronary artery disease (CAD) can be diagnosed by comparing myocardial perfusion scans with a database defining the lower limit of normal myocardial blood flow and flow reserve (MFR). Both13N-ammonia and82rubidium tracers can be used to generate flow images, however only13N-ammonia has been fully validated for quantifying blood flow and MFR using compartmental models. Normal databases have thus only been reported using13N-ammonia PET and compartmental modeling. This study aimed to establish a lower limit of normal MFR for an82Rb database using a compartmental model, and to determine if a simplified model would reduce the measured range of normal MFR for both tracers, improving identification of regional flow defects. 14 subjects with82Rb and 13N-ammonia dynamic PET imaging in a randomized order within a 2-week period. MBF was quantified using a one-compartment model for82Rb, and a two- compartment model for13N-ammonia. A simplified model was used to estimate the net retention rate for both tracers. Model- specific extraction functions were determined to obtain flow estimates. It was found that the retention reserve variability the was lowest and was equivalent for both tracers (plusmn 15% globally, plusmn 16% regionally) indicating that the retention model may be preferable for detection and localization of flow reductions. The two-compartment model for13N-ammonia had the smallest normal MFR range (mean-2sd = 2.27 globally, 1.48 regionally) confirming its precision for absolute flow quantification.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".