A Bayesian Interpretation of a Pediatric Cardiac Arrest Trial (THAPCA-OH)
Bibliographic record
Abstract
BACKGROUND Pediatric out-of-hospital cardiac arrest results in high morbidity and mortality.Currently, there are no recommended therapies beyond supportive care.The THAPCA-OH (Therapeutic Hypothermia after Pediatric Cardiac Arrest Out-of-Hospital) trial compared hypothermia (33.0 C) with normothermia (36.8 C) in 295 children.Good neurobehavioral outcome and survival at 1 year were higher in the hypothermia group (20 vs. 12% and 38 vs. 29%, respectively).These differences did not meet the planned statistical threshold of P,0.05.To ensure that a potentially efficacious therapy is not prematurely discarded, we reassessed THAPCA-OH using a Bayesian statistical perspective. METHODSWe performed a Bayesian analysis, interpreting the trial in probabilistic terms (i.e., the probability that therapeutic hypothermia had any benefit, and overall absolute improvements greater than 2%, 5%, and 10% for 1-year neurobehavioral outcome and survival).Our primary analyses used noninformative priors, meaning that the analyses were based on the observed trial data without any information added by the priors.In the absence of pediatric trials to derive informative prior distributions, we used:(1) downweighted priors from adult trials; and (2) a previously published set of critical care priors that span benefit, equipoise, and harm. RESULTSIn the primary analyses, the probability of any benefit from hypothermia was 94% for both the neurobehavioral outcome and survival at 1 year.For both outcomes, the probability of benefit was .75% for all informative prior integrations with the THAPCA-OH results, except those with the most pessimistic priors.CONCLUSIONS There is a high probability that hypothermia provides a modest benefit in neurobehavioral outcome and survival at 1 year.(ClinicalTrials.govnumber, NCT00878644.)
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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 | 0.089 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".