Safety of very early sheath removal in patients treated with REG1 for acute coronary syndromes: insights from the RADAR trial.
Bibliographic record
Abstract
BACKGROUND: RADAR compared REG1 (25%, 50%, 75%, 100% reversal) with unfractionated heparin (UFH) in 640 acute coronary syndrome (ACS) patients (479 REG1 patients, 161 UFH patients) undergoing an invasive management strategy. We sought to determine whether the REG1 anticoagulation system allows for safer early arterial sheath removal following cardiac catheterization. METHODS: REG1 patients had arterial sheath removal immediately post catheterization. We measured arterial sheath management outcomes and vascular access complications in patients who had sheath removal without vascular closure device implantation; 461 patients were included (349 REG1 patients, 112 UFH patients). RESULTS: The median (25th, 75th) time from end of catheterization to arterial sheath removal was shorter in REG1 arms regardless of reversal strategy (26 minutes [18, 46]) compared with UFH (210 minutes [102, 342]). There was no increase in median time from sheath removal to hemostasis (10 minutes [10, 20] and 10 minutes [10, 20]; P=.60); vascular access-site bleeding complications were numerically fewer with REG1 than UFH (6% vs 11%; odds ratio [OR], 0.57; 95% CI, 0.27-1.18; P=.14). There were no differences in time to ambulation or hospital length of stay between the groups. CONCLUSIONS: REG1 allows for very early arterial sheath removal following cardiac catheterization without increasing the time to hemostasis or vascular access-site bleeding complications. Further studies are needed to determine whether anticoagulation with REG1 will translate into shorter hospital lengths of stay and reduced costs in ACS 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".