Meta‐analysis of risk and protective factors for gastrointestinal bleeding after percutaneous coronary intervention
Bibliographic record
Abstract
AIM: To quantitatively analyse factors related to gastrointestinal bleeding after percutaneous coronary intervention and provide evidence for the prevention of gastrointestinal bleeding. DATA SOURCES AND REVIEW METHODS: Cochrane Library, Pubmed, Embase, and Ovid databases were searched from inception to 31 May 2018; case-control and cohort studies published in English were included. The methodological quality of each study was assessed by two independent reviewers using the Newcastle-Ottawa Scale. Meta-analysis was performed using Revman version 5.3. RESULTS: A total of 16 publications yielded data about risk factors. It was found that age older than 70 years, age (per 10-year increase), female sex, baseline anaemia, history of smoking, history of using alcohol, history of peptic ulcer disease, chronic renal failure, previous bleeding, shock, congestive heart failure, acute myocardial infarction, prior use of inotropic medications, and prior use of antithrombotic medications were positively associated with gastrointestinal bleeding. Four articles yielded data about protective factors. It was found that proton-pump inhibitor and bivalirudin therapy were negatively associated with gastrointestinal bleeding after percutaneous coronary intervention. CONCLUSION: This research found risk and protective factors which can assist in effective management of this potentially fatal complication.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".