Bleeding complications in patients undergoing percutaneous coronary interventions
Bibliographic record
Abstract
Bleeding complications are among the most common complications of percutaneous coronary intervention (PCI) procedures. A multitude of studies carried out over the last decade have confirmed that bleeding complications after PCI have a negative impact on patients' outcome (dissatisfaction, morbidity, and mortality) and hospital indices (length of stay and costs). Apart from better recognition and classification of bleeding, recent research has helped to device several risk stratification tools that have markedly improved prediction of peri-PCI bleeding. Moreover, parallel with the recognition of the deleterious effects of peri-PCI bleeding, several strategies (pre-PCI risk stratification for bleeding, the use of bivalirudin as an antithrombotic/anticoagulant strategy, the radial artery route for vascular access and vascular closure devices) that aim to reduce peri-PCI bleeding were developed and used. Their application has markedly reduced the incidence of bleeding and improved the clinical outcome. In this review, we focus primarily on the bleeding complications occurring during PCI procedures. Specifically, we summarize recent research on the need for a consensus in bleeding definition, incidence of bleeding events, and their impact on outcome, factors associated with increased risk and risk stratification for bleeding, putative mechanisms through which bleeding impact on outcome, and bleeding-avoidance strategies to be used in the setting of PCI procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".