Radial Artery and Ulnar Artery Occlusions Following Coronary Procedures and the Impact of Anticoagulation: <i>ARTEMIS</i> (Radial and Ulnar <i>ARTE</i> ry Occlusion <i>M</i> eta‐Analys <i>IS</i> ) Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Incidence of radial artery occclusions (RAO) and ulnar artery occclusions (UAO) in coronary procedures, factors predisposing to forearm arteries occlusion, and the benefit of anticoaggulation vary significantly in existing literature. We sought to determine the incidence of RAO/UAO and the impact of anticoagulation intensity. Methods and Results Meta‐analysis of 112 studies assessing RAO and/or UAO (N=46 631) were included. Overall, there was no difference between crude RAO and UAO rates (5.2%; 95% confidence interval [CI], 4.4–6.0 versus 4.0%; 95% CI, 2.8–5.8; P =0.171). The early occlusion rate (in‐hospital or within 7 days after procedure) was higher than the late occlusion rate. The detection rate of occlusion was higher with vascular ultrasonography compared with clinical evaluation only. Low‐dose heparin was associated with a significantly higher RAO rate compared with high‐dose heparin (7.2%; 95% CI, 5.5–9.4 versus 4.3%; 95% CI, 3.5–5.3; Q=8.81; P =0.003). Early occlusions in low‐dose heparin cohorts mounted at 8.0% (95% CI, 6.1–10.6). The RAO rate was higher after diagnostic angiographies compared with coronary interventions, presumably attributed to the higher intensity of anticoagulation in the latter group. Hemostatic techniques (patent versus nonpatent hemostasis), geography (US versus non‐US cohorts) and sheath size did not impact on vessel patency. Conclusions RAO and UAO occur with similar frequency and in the order of 7% to 8% when evaluated early by vascular ultrasonography following coronary procedures. More‐intensive anticoagulation is protective. Late recanalization occurs in a substantial minority of patients.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".