Non-pharmacological interventions to improve the success of electrical cardioversion in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background: Electrical cardioversion (ECV) is a common management strategy for atrial fibrillation (AF). ECV contains multiple modifiable components, including pad size and placement, shock energy, and waveform phases. Guidance on optimal technique, however, is limited. A comprehensive review of interventions used to increase ECV success will guide ideal clinical practice and identify techniques that could be studied in future randomized controlled trials. Methods: This review will include RCTs randomized controlled trial and observational studies that compare acute cardioversion success between 2 or more non-pharmacological interventions in patients with AF undergoing ECV. We will search CENTRAL, MEDLINE and EMBASE from inception to present and will also include the grey literature as part of our search. We will assess the risk of bias for each study and summarize the extracted data in a narrative report with a table of findings included. Results: Based on a full search strategy developed for MEDLINE along with findings from the grey literature search, the non-pharmacological interventions are expected to include pad placement, shock energy, waveform phases, and manual pressure, among others. Conclusions: The objective of this scoping review is to identify and examine the evidence on non-pharmacological interventions to improve electrical cardioversion in patients with AF. Registered on Open Science Framework: https://osf.io/gdh27/
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".