M2. Cardiorenal anaemia syndrome vs left ventricular ejection fraction: which is a better mortality prognostic factor for heart failure? A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Aim Cardiorenal anaemia syndrome (CRAS) involves a vicious interaction between heart failure (HF), chronic kidney disease, and anaemia. CRAS is associated with higher morbidity, mortality, and health care cost. This study investigated CRAS as a predicting factor of all-cause mortality compared to left ventricular ejection fraction (LVEF) in HF patients as one of the established classifications being linked to prognosis in HF. Methods and Results A systematic review based on PRISMA guidelines was performed on 6 scientific databases (PubMed, ProQuest, Scopus, ScienceDirect, Springer) with search terms related to left ventricular ejection fraction, heart failure, and cardiorenal anaemia syndrome. Original English case-control and cohort studies in heart failure participants which analyzed hazard ratio (HR) of all-cause mortality were considered eligible. The quality assessment was evaluated with New Castle Ottawa Scale (NOS). A total of 17 studies comprising 90,092 participants from more than 14 countries were included in the qualitative analysis with eleven subjected to meta-analysis. The presence of CRAS aligns with a significantly higher mortality rate (HR 1.64 [95% CI 1.44 – 1.87]) as compared to current classification based on ejection fraction obtained insignificant clinical outcomes of reduced ejection fraction HF (HFrEF) (HR 1.06 [95% CI 0.98 – 1.15]) in the pooled analysis. Conclusion CRAS is a significant death prognosticator and indicates a worse prognosis in HF patients as compared to HF patients with reduced ejection fraction. CRAS is a potential alternative prognostic factor for heart failure patients to predict mortality outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".