A knowledge translation collaborative to improve the use of therapeutic hypothermia in post-cardiac arrest patients: protocol for a stepped wedge randomized trial
Bibliographic record
Abstract
BACKGROUND: Advances in resuscitation science have dramatically improved survival rates following cardiac arrest. However, about 60% of adults that regain spontaneous circulation die before leaving the hospital. Recently it has been shown that inducing hypothermia in cardiac arrest survivors immediately following their arrival in hospital can dramatically improve both overall survival and neurological outcomes. Despite the strong evidence for its efficacy and the apparent simplicity of this intervention, recent surveys show that therapeutic hypothermia is delivered inconsistently, incompletely, and often with delay. METHODS AND DESIGN: This study will evaluate a multi-faceted knowledge translation strategy designed to increase the utilization rate of induced hypothermia in survivors of cardiac arrest across a network of 37 hospitals in Southwestern Ontario, Canada. The study is designed as a stepped wedge randomized trial lasting two years. Individual hospitals will be randomly assigned to four different wedges that will receive the active knowledge translation strategy according to a sequential rollout over a number of time periods. By the end of the study, all hospitals will have received the intervention. The primary aim is to measure the effectiveness of a multifaceted knowledge translation plan involving education, reminders, and audit-feedback for improving the use of induced hypothermia in survivors of cardiac arrest presenting to the emergency department. The primary outcome is the proportion of eligible OHCA patients that are cooled to a body temperature of 32 to 34°C within six hours of arrival in the hospital. Secondary outcomes will include process of care measures and clinical outcomes. DISCUSSION: Inducing hypothermia in cardiac arrest survivors immediately following their arrival to hospital has been shown to dramatically improve both overall survival and neurological outcomes. However, this lifesaving treatment is frequently not applied in practice. If this trial is positive, our results will have broad implications by showing that a knowledge translation strategy shared across a collaborative network of hospitals can increase the number of patients that receive this lifesaving intervention in a timely manner. TRIAL REGISTRATION: ClinicalTrials.gov Trial Identifier: NCT00683683.
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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 | 0.058 | 0.057 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".