Platelet-To-Lymphocyte Ratio Efficiency in Predicting Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention in Acute Coronary Syndromes: A Meta-Analysis
Bibliographic record
Abstract
Background: The platelet-to-lymphocyte ratio (PLR) is applied as a potential first-line prognostic predictor for many cardiovascular diseases due to its simplicity and accessibility. This meta-analysis aimed to quantify the predictive power of PLR for major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), explore its predictive efficacy in different populations, and identify other potential influencing factors. Methods: PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched for eligible studies until February 7, 2025, based on the inclusion and exclusion criteria. The Newcastle-Ottawa scale (NOS) was employed for quality assessment. Sensitivity, specificity, summary receiving operating characteristic (SROC) and area under the curve (AUC) were combined using Stata 15.1 and Meta-DiSc software. Meta-regression analyses, subgroup analyses, threshold effect analyses, sensitivity analyses, and publication bias tests were performed. Results: = 0.05) might be sources of heterogeneity in the specificity analyses. Conclusions: High PLR levels have favorable values in predicting in-hospital and long-term MACEs after PCI in ACS patients. The PLR had greater sensitivity and an improved ability to identify risk in patients aged >60 years and the subgroup with a higher proportion of women and was also more sensitive to in-hospital MACEs. The PROSPERO Registration: No. CRD42024537586, https://www.crd.york.ac.uk/PROSPERO/view/CRD42024537586.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.069 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".