Identifying the value of RVEF for the prediction of major cardiovascular outcomes: a study of 7,131 patients undergoing cardiovascular magnetic resonance imaging
Bibliographic record
Abstract
Abstract Background Right ventricular (RV) function remains poorly recognized for its value in predicting cardiovascular events at a population level. Cardiovascular Magnetic Resonance (CMR) imaging is the gold standard for RV assessment. Purpose To define the independent prognostic value of RVEF for the prediction of major adverse cardiovascular events (MACE) as primary outcome in patients with known or suspected cardiovascular disease. Methods Data was obtained from the Cardiovascular Imaging Registry of Calgary (CIROC). Patients underwent standardized CMR imaging protocols and analysis. Clinical events were identified from administrative data. Results 7,131 patients were included. 870 primary outcome events occurred over 2.5 years follow-up. RVEF provided equivalent predictive utility versus LVEF (Table 1). There was an increase in events with worsening severity of RVEF (Figure 1), with a significant “threshold-effect” at an RVEF of 40%. Conclusions RVEF is a strong and independent predictor of MACE at a population level. Figure 1 Funding Acknowledgement Type of funding source: None
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".