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 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 | 0.049 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".