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 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".