Heparin dosing in uninterrupted anticoagulation with dabigatran vs. warfarin in atrial fibrillation ablation: RE-CIRCUIT study
Bibliographic record
Abstract
AIMS: To describe heparin dosing requirements in patients who underwent catheter ablation of atrial fibrillation with uninterrupted anticoagulation using dabigatran etexilate (dabigatran) or warfarin to attain therapeutic activated clotting time (ACT) in the RE-CIRCUIT® study. The RE-CIRCUIT study showed significantly fewer major bleeding events in the dabigatran vs. warfarin treatment group. Unfractionated heparin was administered during the procedure to maintain ACT >300 s. METHODS AND RESULTS: Patients were randomly assigned to dabigatran 150 mg bid or international normalized ratio-adjusted warfarin. Ablation was performed with uninterrupted anticoagulation and continued for 8 weeks after the procedure. Heparin was administered after placement of femoral sheaths before or immediately after transseptal puncture. Ablation was performed in 635 patients (dabigatran, 317; warfarin, 318); data were available from 396 patients administered heparin (dabigatran, 191; warfarin, 205). Most frequent time window from last dose of study drug to septal puncture was 0 to <4 h in the dabigatran (41.3%) and 16 to <24 h in the warfarin arms (44.7%). Overall mean (standard deviation) heparin dose was similar between the dabigatran and warfarin groups [12 402 (10 721) vs. 11 910 (8359) IU, respectively]. Heparin dosing requirement to reach therapeutic ACT was lowest when time from last dose of dabigatran to septal puncture was 0 to <4 h. CONCLUSION: Patients treated with dabigatran required a similar amount of unfractionated heparin as those treated with warfarin to achieve an ACT of >300 s during ablation. More heparin units were required when the time from the last dose of dabigatran to septal puncture increased.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".