Depressed Exercise Peak Ejection Rate Detected on Ambulatory Radionuclide Monitoring Reflects End-Stage Cardiac Inotropic Reserve and Predicts Mortality in Ischaemic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Fifteen patients with ischaemic cardiomyopathy and inducible ischaemia were studied to determine the mechanisms of mortality. Failure of the contractile reserve during daily life activities may reflect a prognostic index. METHODS: Single photon emission cardiac tomography and radionuclide ambulatory monitoring (Vest) data were analysed in all patients with a 7-year follow-up. RESULTS: At peak exercise on Vest, the 7 non-survivors (N-SURV) showed worse peak ejection rates (PERs) and ejection fractions (EFs) compared with the 8 survivors (SURV), (2 ± 0.6 vs. 3.3 ± 0.7; end-diastolic volumes (EDVs), P < 0.003), and (34 ± 10% vs. 50 ± 13%; P < 0.02), respectively. However, exercise peak filling rates (PFRs) (1.9 ± 0.6 vs. 2.7 ± 0.9; EDVs/s) and exercise heart rates (HRs), (97 ± 17 vs. 106 ± 10), did not differ between the two groups (P > 0.05). In SURV, exercise PERs, which represented rapid left ventricular (LV) emptying, were significantly correlated with exercise PFRs, representing rapid LV filling, (r = 0.71, P < 0.04) but not in N-SURV (r = 0.66, P > 0.05). Among SURV, the Frank-Starling mechanism was thus preserved but not in N-SURV. Upon Cox analysis, overall LV function parameters, exercise PER was the only predictive measure associated with mortality (b = - 0.018, relative hazard ratio = 0.98, P = 0.02). CONCLUSIONS: Exercise PER reduced values reflected failure of the Frank-Starling mechanism, the incapacity of the heart to perform rapid contractile adaptations to daily life activities and a poor prognosis.
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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 | 0.000 | 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) | 0.001 | 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".