Detection of myocardial perfusion abnormalities after a recent acute coronary syndrome by quantitative Levovist myocardial contrast echocardiography: comparison with 99m Tc-Myoview SPECT imaging.
Bibliographic record
Abstract
BACKGROUND: The value of stress harmonic power Doppler imaging (HPDI) for the evaluation of myocardial perfusion has never been assessed in patients after acute coronary syndrome (ACS). OBJECTIVE: To evaluate the agreement between stress HPDI and single photon emission computed tomography (SPECT) imaging for the assessment of myocardial perfusion after unstable angina or myocardial infarction. PATIENTS AND METHODS: Thirty patients with a recent ACS underwent HPDI and SPECT. Images were obtained at rest and during dipyridamole infusion (0.56 mg/kg over 4 min). Apical two- and four-chamber views were used for HPDI. Ten myocardial segments were scored for myocardial perfusion. Semiquantitative and quantitative video intensity analysis with background subtraction were performed. RESULTS: Concordance by patients between quantitative HPDI and SPECT was 76% (kappa=0.40, Phi=0.46) for normal versus abnormal perfusion. When semiquantitative analysis was used, concordance was 72% (kappa=0.42, Phi=0.46). Agreement between methods was best in the left anterior descending artery territory for quantitative (80%) (kappa=0.60, Phi=0.60) and semiquantitative analysis (78%) (kappa=0.51, Phi=0.60) for normal versus abnormal perfusion. Discrepancies between HPDI and SPECT were most important in the circumflex territory, with a concordance of 59% (kappa=0.22) for identification of normal perfusion versus irreversible and reversible defects. CONCLUSIONS: These results suggest that HPDI can detect myocardial perfusion at rest and during pharmacological stress in patients after a recent ACS. Given the suboptimal agreement with SPECT, further advances are required before the routine use of contrast echocardiography is possible for the assessment of myocardial perfusion.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".