Automatic and visual reproducibility of perfusion and function measures for myocardial perfusion SPECT
Bibliographic record
Abstract
BACKGROUND: We define the repeatability coefficients (RC) of key quantitative and visual perfusion and function parameters that can be derived by the QGS/QPS automated software and by expert visual observer from gated myocardial perfusion SPECT (MPS) scans. METHODS: Standard QGS/QPS algorithms have been applied to derive quantitative perfusion and function parameters in 200 99mTc-tetrofosmin rest/stress MPS scans, obtained in 100 consecutive patients who underwent 2 separate gated rest/stress scans on the same camera. Variables included stress, rest, and ischemic total perfusion deficit (TPD), ejection fraction, motion, and thickening. Visual perfusion/motion scores were derived by an expert reader using randomized scan order and normalized to % myocardium. RESULTS: Quantitative and visual parameters were highly reproducible with smaller RC for some quantitative measures as compared to visual measures (P < .0001). RC for quantitative measures were 3.3% for stress TPD, 1.8% for rest TPD, and 3.2% for ischemic TPD and for visual scoring 4.8% for stress, 3.8% for rest, and 4.3% for ischemic (P ≤ .002). The results in each vessel territory showed that in the right coronary artery (RCA) territory the quantitative approach had improved reproducibility as compared to visual reading. Visual thickening scoring was more reproducible than motion scoring (P < .0001). CONCLUSIONS: This study demonstrates that standard perfusion and function parameters derived from MPS by visual or quantitative analysis are highly reproducible with some advantages to the quantitative approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".