Double sequential external defibrillation for refractory ventricular fibrillation: the science, the controversies and the future.
Bibliographic record
Abstract
Double sequential external defibrillation (DSED), the technique of providing two rapid shocks from two defibrillators with pads placed in the anterior-lateral and anterior-posterior position has been studied in animal labs and clinical practice for over two decades. In 2022, the Double Sequential External Defibrillation for Refractory Ventricular Fibrillation (DOSE-VF, clinicaltrials.gov: NCT04080986) trial was published in the New England Journal of Medicine. This cluster randomized crossover trial involved six paramedic services in Ontario, Canada, and compared standard (anterior-lateral) defibrillation to vector change defibrillation (VC, anterior-posterior pad repositioning) or DSED for patients with ventricular fibrillation (VF) and unresponsive to three standard shocks. The trial showed superior outcomes for all primary and secondary endpoints (VF termination, return of spontaneous circulation [ROSC], survival to hospital discharge, and neurologically intact survival) with DSED versus standard defibrillation, and improved VF termination and survival to discharge, but not ROSC or neurologically intact survival, with VC versus standard defibrillation. These findings, along with the 2023 updated ILCOR guidelines endorsing DSED for refractory VF, have generated significant global interest in it's implementation. This manuscript explores the scientific rationale and underlying mechanisms of DSED, examines controversies surrounding its implementation, and outlines directions for future research.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".