Prognostic Value of Red Blood Cell Distribution Width for Patients with Heart Failure: A Systematic Review and Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
AIMS: Multiple studies have investigated the prognostic role of red blood cell distribution width (RDW) for patients with heart failure (HF), but the results have been inconsistent. The aim of the present study was to estimate the impact of RDW on the prognosis of HF by performing a systematic review and meta-analysis. METHODS AND RESULTS: The Embase, PubMed, and Web of Science databases were searched up to November 16, 2013 to identify eligible cohort studies. The quality of each study was assessed using the Newcastle-Ottawa Scale (NOS). The association between RDW, either on admission or at discharge, and HF outcomes (all-cause mortality [ACM], heart transplantation, cardiovascular mortality, and rehospitalization, etc.) were reviewed. The overall hazard ratio (HR) for the effect of RDW on ACM was pooled using a random-effects model, and the publication bias was evaluated using funnel plots and Eggers' tests. Seventeen studies, with a total of 18288 HF patients, were included for systematic review. All eligible studies indicated that RDW on admission and RDW at discharge, as well as its change during treatment, were of prognostic significance for HF patients. The HR for the effect of a 1% increase in baseline RDW on ACM was 1.10 (95% confidence interval: 1.07-1.13), based on pooling of nine studies that provided related data. However, publication bias was observed among these studies. CONCLUSIONS: HF patients with higher RDW may have poorer prognosis than those with lower RDW. Further studies are needed to explore the potential mechanisms underlying this association.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".