Red Cell Distribution Width and Mortality in Patients with Acute Coronary Syndrome: A Meta-Analysis on Prognosis
Bibliographic record
Abstract
BACKGROUND: Red cell distribution width (RDW), a routine component of the CBC, measures variation in the size of circulating erythrocytes. It has been associated with several clinical outcomes in cardiovascular (CV) disease. We aimed to describe the association between RDW and mortality in patients admitted for acute coronary syndrome (ACS) through pooled analysis. Methods: This study was a meta-analysis of observational studies that included reported data on all-cause or CV mortality associated with RDW in patients admitted for ACS and used logistic regression analysis to control for confounders. A search for eligible studies was conducted Using MEDLINE, Clinical Key, ScienceDirect, Scopus and Cochrane Central Register of Controlled Trials databases. The quality of each study was evaluated using the Newcastle-Ottawa Quality Assessment Scale. Using RevMan version 5.3, we performed Mantel-Haenzel analysis of random effects to determine the association of RDW with all-cause or CV mortality and major adverse cardiovascular events (MACE). Results: We identified 13 trials comprising 10,410 ACS patients. Pooled analysis showed that a low RDW was associated with a significantly lower all-cause or CV mortality (RR 0.35; 95% CI 0.30-0.40); p<0.00001; I2=53%). A low RDW was also associated with a lower risk for MACEs after an ACS (RR 0.56; 95% CI 0.51-0.61; p<0.00001; I2=91%]. Conclusion: A low RDW during an ACS is associated with lower all-cause or CV mortality and lower risk of subsequent MACEs, providing us with a convenient and inexpensive risk stratification tool in ACS patients. Keywords: red cell distribution width, acute coronary syndrome, myocardial infarction.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.002 | 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".