Exercise oscillatory ventilation (EOV) as a predictor of mortality in patients with amyloid cardiomyopathy
Bibliographic record
Abstract
Abstract Background Exercise oscillatory ventilation (EOV) signals multiple abnormalities in hemodynamics and ventilatory control which may predict poor outcome in patients with cardiac diseases. Aim To assess whether EOV (+) patients with amyloid cardiomyopathy show higher mortality rates compared to their counterparts without EOV. Methods We analyzed cardiopulmonary exercise testing for EOV, defined by continuous (>60% of exercise duration) oscillations in minute ventilation (VE), cycling with a frequency of one minute and an amplitude above 15% of resting VE. Results 156 patients with amyloid cardiomyopathy, aged 78 (±7) years, 87% male with a NYHA functional class of II (49.4%) and median B-type natriuretic peptide levels of 2548 (1374-4703) ng/L, had a mean peak oxygen consumption of 14.8 (±4.42) ml/kg·min and a VE to carbon dioxide production (VCO2) slope ≥ 40 in 67.9% of cases. EOV was seen in 38 (24.4%), clearly absent in 92 (59.0%) and not clearly differentiable in 26 (16.7%) patients. 33 (21.2%) had a HF-related hospitalization and 31 (19.9%) died. EOV shows a hazard ratio of 2.35 (CI 1.088 – 5.075, p-value <0.05) for all-cause mortality. In fact, the log-rank test showed a difference in survival time between patients with and without EOV (p=0.03) with a median survival time of 46 months. Conclusion EOV may improve the prognostic yield of incremental CPET in patients with amyloid cardiomyopathy.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".