A Bayesian Perspective Extracorporeal CPR for Refractory Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Abstract Background Whether extracorporeal CPR (eCPR) has survival benefits over conventional CPR (cCPR) in patients with refractory out-of-hospital cardiac arrest is an unresolved clinical question. Performing trials in this environment is exceedingly challenging and inferences need careful examination. Objective Determine if a Bayesian perspective provides additional inferential insights. Methods The INCEPTION trial of patients with refractory out-of-hospital cardiac arrest reported eCPR and cCPR had similar effects on the primary outcome, 30 day survival with a favorable neurologic outcome. Herein the probability of eCPR superiority, equivalence or inferiority to cCPR is re-evaluated with a Bayesian analysis using both vague and informative priors (from previously completed randomized clinical trials (RCTs)). Results Depending on the chosen prior, the Bayesian reanalysis of the INCEPTION intention-to-treat (ITT) data suggests an equivalence probability < 10% (defined as an absolute risk difference (RD) < 1%) but a clinical superiority probability of 66 - 99 % (defined as RD > 1.0). An INCEPTION per protocol (PP) analysis with a vague prior suggested a 1% probability of clinical benefit but this posterior probability increased to 86% when informative PP data from previous RCTs were considered. Conclusion Bayesian INCEPTION trial re-analyses provide additional quantative insights. The totality of the ITT evidence reveals a high probability for a clinically meaningful eCPR benefit over cCPR at 30 days. A PP analysis shows a less definitive probability of benefit. (Abstract word count 197, Manuscript word count 1477)
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 | 0.078 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".