Evolution of Technology, Establishment of Program, and Clinical Outcomes in Pediatric Extracorporeal Membrane Oxygenation: The “SickKids” Experience
Bibliographic record
Abstract
Technological development has had a tremendous impact on the management of patients who require extracorporeal membrane oxygenation (ECMO). Team development and education are a vital component of a successful extracorporeal life support (ECLS) Program to reduce complications and subsequently improve clinical outcomes. We sought to review the evolution in technology, importance of team development and training, and report our experience at The Hospital for Sick Children, Toronto. There were a total of 576 ECMO runs in 534 patients (42 repeat ECMO runs) between January 1988 and June 2012. The use of ECMO for cardiac disease has increased in the last decade due to an expanded indication for ECMO in patients with single-ventricle physiology. Cardiac ECMO still remains a challenge in terms of survival (177/392, 45%). Although development of an ECLS program and team education facilitated extracorporeal cardiopulmonary resuscitation, clinical outcomes were not satisfactory (survival, 33%). The most common complications were hemorrhagic (13.8%), followed by renal (10.6%) and pulmonary dysfunction (6.9%). Advances in technology made management during ECMO safer, and the mechanical complications related to the ECMO system were 6.1%, including circuit changes due to thrombus formation, cannula repositioning, or optimization of size.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".