Designing and maintaining a rescue extracorporeal life support program: A holistic simulation approach
Bibliographic record
Abstract
Rescue Extracorporeal Life Support Programs based at non-cardiac surgery centers have unique needs to be able to ensure successful outcomes despite low patient volumes. In this paper we describe the important role simulation had in each stage of development, implementation, and maintenance of our pediatric Rescue ECLS Program. Systems-focused simulations were used to develop robust workflows, processes, and bundles. Simulation-based education targeted the acquisition and maintenance of clinical skills for individual team members, bringing together a multidisciplinary team of local clinicians who do not routinely perform pediatric cannulation related tasks. Translational simulation ensured continued improvement by addressing adverse events or latent safety threats observed during system-focused or educational simulations. Realism of all simulations was our priority, and was achieved through in situ simulations, participation of multidisciplinary teams, use of real equipment and medical supplies, and use of a high-fidelity cannulation manikin. This holistic simulation approach allowed us to overcome the barriers to high quality care, and maintain outcomes comparable to high volume centers. A similar approach can help other centers design simulation for their own Rescue ECLS Program, and can be translated to other high-risk and high-acuity critical care programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".