Myocardial blood flow quantification with SPECT
Bibliographic record
Abstract
INTRODUCTION: The addition of absolute myocardial blood flow (MBF) data improves the diagnostic and prognostic accuracy of relative perfusion imaging with nuclear medicine. Cardiac-specific gamma cameras allow measurement of MBF with SPECT. METHODS: This paper reviews the evidence supporting the use of SPECT to measure myocardial blood flow (MBF). Studies have evaluated SPECT MBF in large animal models and compared it in humans with invasive angiographic measurements and against the clinical standard of PET MBF. The repeatability of SPECT MBF has been determined in both single-site and multi-center trials. RESULTS: SPECT MBF has excellent correlation with microspheres in an animal model, with the number of stenoses and fractional flow reserve, and with PET-derived MBF. The inter-user coefficient of variability is ∼20% while the COV of test-retest MBF is ∼30%. SPECT MBF improves the sensitivity and specificity of the detection of multi-vessel disease over relative perfusion imaging and provides incremental value in predicting adverse cardiac events. CONCLUSION: SPECT MBF is a promising technique for providing clinically valuable information in the assessment of coronary artery disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".