Prognostic value of red cell distribution width in patients undergoing percutaneous coronary intervention: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prognostic value of baseline red cell distribution width (RDW) in patients with coronary artery diseases (CADs) undergoing percutaneous coronary intervention (PCI) by conducting a meta-analysis. DESIGN: Systematic review and meta-analysis. DATA SOURCE: PubMed, Embase, Wanfang, CNKI and VIP databases were searched from their inceptions to 19 June 2019. ELIGIBLE CRITERIA: Studies investigating the value of baseline RDW for predicting all-cause mortality, cardiovascular mortality and major adverse cardiac events (MACEs) in patients with CAD undergoing PCI were included. DATA EXTRACTION AND SYNTHESIS: Two authors independently extracted the data and evaluated the methodological quality using the Newcastle-Ottawa Scale. STATA V.12.0 software was applied to produce the forest plots using a random-effect model. RESULTS: Twelve studies (13 articles) involving 17 113 patients were included and analysed. Comparison between the highest and lowest RDW category indicated that the pooled risk ratio (RR) was 1.77 (95% CI 1.32 to 2.37) for all-cause mortality, 1.70 (95% CI 1.25 to 2.32) for cardiovascular mortality and 1.62 (95% CI 1.21 to 2.18) for MACEs. The predictive effect of elevated RDW for all-cause mortality was stronger in the subgroup of patients without anaemia (RR 4.59; 95% CI 3.07 to 6.86) than with anaemia. CONCLUSIONS: This meta-analysis indicated that elevated RDW was associated with higher risk of mortality and adverse cardiac events in patients with CAD undergoing PCI. The value of elevated RDW for predicting all-cause mortality appears to be stronger in patients without anaemia. RDW may be served as a promising prognostic biomarker in patients undergoing PCI.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.048 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".