P1684Risks of cardiac implantable electronic device procedures performed with uninterrupted direct oral anticoagulant therapy
Bibliographic record
Abstract
Background/Introduction: Clinicians performing cardiac implantable electronic device (CIED) procedures in patients treated with chronic oral anticoagulants (OAC) balance arterio-thromboembolic (ATE) risks with bleeding complications. Recent studies demonstrated that uninterrupted warfarin in patients undergoing CIED procedures, reduced significantly the rate of major hematoma while protecting patients against ATE events. Direct oral anticoagulants (DOAC) are increasingly being used and little published literature exists describing the risks of uninterrupted DOAC therapy in patients undergoing CIED procedures. Purpose: This study aims to demonstrate the risks of undergoing CIED procedures using a strategy of uninterrupted DOAC. Methods: A retrospective cohort study performed in a single Canadian university teaching center from January 2013 to June 2016 was conducted. We studied the risk of complications associated with CIED procedures performed with uninterrupted DOAC therapy compared to all other possible strategies. A multivariate logistic regression analysis adjusted for propensity scores was used. Our primary outcome was defined as either significant hematoma, ATE events, pericardial effusions or tamponades or death.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".