Prevalence rate of laboratory defined aspirin resistance in cardiovascular disease patients: A systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the first cause of mortality worldwide, with all the healthcare systems facing this very challenging issue. Aspirin continues to be the major gold-standard treatment worldwide in the prevention of thrombotic disease in patients with CVD, even though not all individuals respond to antiplatelet therapy in a similar way, being resistant to aspirin. The aim of this study was to determine the prevalence of laboratory defined aspirin resistance in CVD patients worldwide. METHODS: Relevant articles were identified through searching EMBASE, PubMed/ MEDLINE, ISI /Web of Science, Scopus, and the Cochrane Library, from January 2000 to February 2018. The methodological quality of the included studies was critically appraised using the Newcastle-Ottawa scale. The pooled prevalence of laboratory defined aspirin resistance was computed using the Der Simonian-Laird random-effect model. RESULTS: We included 65 studies, with a total of 10,729 patients. The overall prevalence of laboratory defined aspirin resistance in CVD patients was 24.7% ([95%CI 21.4-28.4]. Women were found to be at increased risk of laboratory defined aspirin resistance compared to men, with an odds ratio of 1.16 [95%CI 0.87-1.54]. CONCLUSION: Doctors and healthcare providers should pay special attention to aspirin resistance since lack of awareness could cause problems and increase mortality in these patients, if not properly treated with higher aspirin doses.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".