Bayesian analysis of amiodarone or lidocaine versus placebo for out-of-hospital cardiac arrest
Bibliographic record
Abstract
OBJECTIVE: Clinical trials for patients with shock-refractory out-of-hospital cardiac arrest (OHCA), including the Amiodarone, Lidocaine or Placebo (ALPS) trial, have been unable to demonstrate definitive benefit after treatment with antiarrhythmic drugs. A Bayesian approach, combining the available evidence, may yield additional insights. METHODS: We conducted a reanalysis of the ALPS trial comparing treatment with amiodarone or lidocaine with placebo in patients with OHCA following shock-refractory ventricular fibrillation or ventricular tachycardia (VF/VT). We used Bayesian regression to assess the probability of improved survival or improved neurological outcome on the 7-point modified Rankin Scale. We derived weak, moderate and strong priors from a previous clinical trial. RESULTS: The original ALPS trial randomised 3026 adult patients with OHCA to amiodarone (n=974, survival to hospital discharge 24.4%), lidocaine, (n=993, survival 23.7%) or placebo (n=1059, survival 21.0%). In our reanalysis the probability of improved survival from amiodarone ranged from 83% (strong prior) to 95% (weak prior) compared with placebo and from 78% (strong) to 90% (weak) for lidocaine-an estimated improvement in survival of 2.9% (IQR 1.4%-3.8%) for amiodarone and 1.7% (IQR 0.84%-3.2%) for lidocaine over placebo (moderate prior). The probability of improved neurological outcome from amiodarone ranged from 96% (weak) to 99% (strong) compared with placebo and from 88% (weak) to 96% (strong) for lidocaine. CONCLUSIONS: In a Bayesian reanalysis of patients with shock-resistant VF/VT OHCA, treatment with amiodarone had high probabilities of improved survival and neurological outcome, while treatment with lidocaine had a more modest benefit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".