Improving Appropriate Neurologic Prognostication after Cardiac Arrest: A Stepped Wedge Cluster Randomized Controlled Trial
Bibliographic record
Abstract
RATIONALE: Predictions about neurologic prognosis that are based on early clinical findings after out-of-hospital cardiac arrest (OHCA) are often inaccurate and may lead to premature decisions to withdraw life-sustaining treatments (LST) in patients who might otherwise survive with good neurologic outcomes. OBJECTIVES: To improve adherence to recommendations for appropriate neurologic prognostication after OHCA and reduce deaths from premature decisions to withdraw LST. METHODS: This was a pragmatic stepped wedge cluster randomized controlled trial evaluating a multifaceted quality intervention (education, pathways, local champions, audit-feedback). The primary outcome was appropriate neurologic prognostication, defined as (1a) no early withdrawal of LST (WLST) (within 72 h) based on estimates of poor neurologic prognosis and (1b) no WLST between 72 hours and 7 days in absence of clinical predictors of poor neurologic prognosis or (2) surviving beyond 7 days. Secondary outcomes were deaths from early WLST and survival with good neurologic outcome. MEASUREMENTS AND MAIN RESULTS: Between June 1, 2011, and June 30, 2014, a total of 905 patients with OHCA were enrolled from ICUs of 18 Ontario hospitals. Rates of appropriate neurologic prognostication increased after the intervention (68% vs. 74% patients; odds ratio [OR], 1.79; 95% confidence interval [CI], 1.01-3.19; P = 0.05). However, rates of survival to hospital discharge (46% vs. 50%; OR, 1.71; 95% CI, 0.97-3.01; P = 0.06) and survival with good neurologic outcome remained similar (38% vs. 43%; OR, 1.43; 95% CI, 0.84-2.86; P = 0.19). CONCLUSIONS: A multicenter quality intervention improved rates of appropriate neurologic prognostication after OHCA but did not increase survival with good neurologic outcome. Clinical trial registered with www.clinicaltrials.gov (NCT 01472458).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".