A survey of the incidence of defibrillator damage during double sequential external defibrillation for refractory ventricular fibrillation
Bibliographic record
Abstract
Background: Double Sequential External Defibrillation (DSED) is a proposed treatment strategy for patients in refractory VF (RVF) during out-of-hospital cardiac arrest (OHCA). Defibrillator damage employing DSED is a theoretical concern expressed by defibrillator manufacturers yet the incidence of damage during resuscitation remains unknown. Objective: We sought to explore the incidence of defibrillator damage employing DSED for RVF during OHCA. Methods: We conducted a survey of EMS agencies, authors of previous publications, EMS medical directors, base hospital medical oversight groups, and defibrillator manufacturers to assess the incidence of defibrillator damage during DSED. Our survey focused on the frequency of DSED use, number of shocks used during DSED, technique used to employ DSED (simultaneous or sequential), and the incidence of defibrillator damage during DSED. We specifically targeted groups that were known to be using DSED in clinical practice. Results: Our survey response rate was 50% (65/129): 61% (34/56) EMS medical directors, 60% (6/10) authors, 100% (8/8) base hospitals, 33% (1/3) defibrillator manufacturers, 31% (16/52) paramedic services. In our case-based analysis the overall incidence of defibrillator damage was 0.4%. The incidence of defibrillator damage based on total number of DSED shocks was estimated between 0.11% and 0.22%. All reported cases of defibrillator damage occurred using a simultaneous defibrillation technique. Conclusion: When DSED is employed using either a sequential or simultaneous technique the rate of defibrillator damage appears to be exceedingly low. Further high-quality evidence is required to determine the impact of DSED on patient centered outcomes, but the incidence of defibrillator damage should not limit it use. Defibrillator damage should continue to be monitored in future trials and clinical practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".