260: Increased Interdisciplinary Compliance with Standardization of ECLS Mobilization Guideline
Bibliographic record
Abstract
Background: Reduced time from ECMO activation to established flow is associated with decreased morbidity and mortality. While ECPR guidelines set flow initiation at <30 minutes, no benchmark exists for non-ECPR cannulations. Our institution’s activation-to-flow time for all cannulations exceeded 60 minutes. We embarked on a quality improvement project to shorten flow initiation time and improve patient outcomes. Methods: The Institute of Healthcare Improvement’s Model for Improvement was the project’s framework. An ECMO cannulation time protocol was implemented through Plan, Do, Study, Act cycles. Our primary aim was to decrease the time from activation to cannulation by 15%. Process measures included times of blood order entry, blood receival, primer arrival, surgeon arrival, time-out, and time of flow. The primary outcome measure was discharge survival. Results: There were 163 cannulations from January 2020 to December 2024. Time from ECMO activation to cannulation improved by 23% (median 95 minutes to 73 minutes). Surgeon arrival time improved by 51% (mean 30.4 to 14.79 minutes). Blood order placement time improved by 34% (mean 6.7 to 4.4 minutes) and blood receival time improved by 50% (mean 49 to 25 minutes). Discharge survival improved by 22% (62.9% to 76.9%). Conclusion: An ECMO cannulation protocol, with defined metrics and time tracking, improved activation-to-flow time. However, systematic barriers persist: specific process measures (primer arrival, time-out) showed no clinically significant improvement, and cannulation time remains >60 minutes. Addressing these barriers is crucial for optimizing efficiency, reducing complications, and improving survival.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".