Known and Missing Left Ventricular Ejection Fraction and Survival in Patients with Heart Failure: A MAGGIC Meta-Analysis Report
Bibliographic record
Abstract
AIMS: Treatment of patients with heart failure (HF) relies on measurement of LVEF. However, the extent to which EF is recorded varies markedly. We sought to characterize the patient group that is missing a measure of EF, and to explore the association between missing EF and outcome. METHODS AND RESULTS: Individual data on 30 445 patients from 28 observational studies in the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) project were used to compare the prevalence of co-morbidities and outcome across three groups of HF patients: those with missing EF (HF-mEF), reduced EF (HF-REF), and preserved EF (HF-PEF). A total of 29% had HF-mEF, 52% HF-REF, and 19% HF-PEF. Compared with patients in whom EF was known, patients with HF-mEF were older, had a greater prevalence of COPD and previous stroke, and were smokers. Patients with HF-mEF were less likely to receive evidence-based treatment than those with HF-REF. Adjusted mortality in HF-mEF was similar to that in HF-REF and greater than that in HF-PEF at 3 years [HF-REF, hazard ratio (HR) 1.03, 95% confidence interval (CI) 0.95-1.12); HF-PEF, HR 0.78, 95% CI 0.71-0.86]. CONCLUSION: Missing EF is common. The short- and long-term outcome of patients with HF-mEF is poor and they exhibit different co-morbidity profiles and treatment patterns compared with patients with known EF. HF patients with missing EF represent a high risk group.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.039 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".