P4697Multi-center clinical evaluation of a precision-controlled rubidium-82 elution system for PET myocardial perfusion imaging
Bibliographic record
Abstract
Introduction: Rubidium-82 (Rb-82) PET myocardial perfusion imaging (MPI) is gaining widespread use due to its superior diagnostic accuracy and its availability without the need for an onsite cyclotron. Rb-82 cardiac PET also enables evaluation of myocardial flow reserve and left ventricular ejection fraction at peak stress, further enhancing patient management with very low radiation exposure. For optimal MPI and added myocardial blood flow (MBF) quantification with low test-retest variability, eluted Rb-82 activity profiles should be accurate, precise and delivered consistently over a relatively short time interval. For patient safety, elutions must be free of breakthrough of the long-lived parent isotope strontium-82 (Sr-82), and Sr-85. We evaluated the performance of a commercial Rb-82 elution system using constant-activity-rate infusions for PET MPI at different imaging centers. Methods: The performance of six Rb-82 elution system units (RUBY-FILL) was evaluated at five PET imaging sites over a 2-year period (1,300 cumulative days of use). N=6,582 patients underwent rest-stress MPI (total = 13,164 scans) with weight-based infusions of 10 MBq/kg. A standard 30s square-wave “constant-activity” infusion, unique to this device, was used to allow for additional flow quantification with 3D dynamic PET imaging. Automated quality control (QC) measurements of Rb-82 yield and Sr breakthrough detection limit values were recorded daily from which precision was estimated. For the patient elutions, bias and precision of the requested vs. delivered activity (MBq) and elution time (s) were determined. Bias and precision values were compared between imaging sites using one-way analysis of variance, with p<0.05 considered statistically significant.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".