Abstract 2951: Estimation of Perfusion with <sup>13</sup> N-Ammonia Retention Underestimates the Frequency and Extent of Hibernating Myocardium when Compared to Quantification of Absolute Myocardial Blood Flow
Bibliographic record
Abstract
Background: Viable dysfunctional myocardium can be classified as chronically stunned (normal resting perfusion) or hibernating (reduced resting flow). While 13 N-ammonia (NH 3 ) retention (late uptake) is typically used to estimate perfusion, the frequency and extent of hibernating myocardium (HM) may differ when quantification of dynamically-acquired absolute myocardial blood flow (MBF) is performed. Methods: Patients with stable ischemic cardiomyopathy (n = 25, EF 32 ± 2%, NYHA Class 2.1 ± 0.7) who were candidates for an ICD for the primary prevention of sudden death underwent imaging with NH 3 and insulin-clamp 18 F-2-deoxyglucose (FDG). Segmental perfusion (17 segment LV model, % peak segment) was assessed by both retention (20 minute summed image) and absolute MBF using a 3-compartment model (ml/min/g). Normal segments were defined as perfusion ≤ 80% peak segment. Segments with <50% peak segment FDG uptake were defined as scar. The remaining segments were considered HM if FDG/perfusion ratio was ≥ 1.2. Results: Of the 425 total segments, only 15 (3.5%) were considered HM when NH 3 retention was used to assess perfusion. In contrast, the number of HM segments increased markedly with quantification of absolute MBF (159 or 37%, p<0.001 vs. retention), with a commensurate reduction in the number of normally-perfused segments. Conclusions: The estimation of perfusion with NH 3 retention significantly overestimates MBF (Figure ) and hence underestimates the frequency and extent of HM. While the differentiation of chronically stunned from HM may not influence the decision for revascularization, the distinction may be important if HM is an independent risk factor for sudden death.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.005 | 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".