The impact of alternate defibrillation strategies on shock-refractory and recurrent ventricular fibrillation: A secondary analysis of the DOSE VF cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The DOSE VF randomized controlled trial (RCT) employed a pragmatic definition of refractory ventricular fibrillation (VF after three successive shocks). However, it remains unclear whether the underlying rhythm during the first three shocks was shock-refractory or recurrent VF. OBJECTIVE: To explore the relationship between alternate defibrillation strategies employed during the DOSE VF RCT and the type of VF, either shock-refractory VF or recurrent VF, on patient outcomes. METHODS: We performed a secondary analysis of the DOSE VF RCT. We categorized cases as shock-refractory or recurrent VF based on pre-randomization shocks (shocks 1-3). We then analyzed all subsequent (post-randomization) shocks to assess the impact of standard, vector change (VC) or double sequential external defibrillation (DSED) shocks on clinical outcomes employing logistic regression adjusted for Utstein variables, antiarrhythmics, and epinephrine. RESULTS: We included 345 patients; 60 (17%) shock-refractory VF, and 285 (83%) recurrent VF. Patients in recurrent VF had greater survival than shock-refractory VF (OR: 2.76 95% CI [1.04, 7.27]). DSED was superior to standard defibrillation for survival overall, and for patients with shock-refractory VF (28.6% vs 0%, p = 0.041) but not for those in recurrent VF. DSED was superior to standard defibrillation for return of spontaneous circulation (ROSC) and neurologic survival for shock-refractory and recurrent VF. VC defibrillation was not superior for survival or ROSC overall, for shock-refractory, or recurrent VF groups, but was superior for VF termination across all groups. CONCLUSION: DSED appears to be the superior defibrillation strategy in the DOSE VF trial, irrespective of whether the preceding VF is shock-refractory or recurrent.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".