CEPP: Canadian Extracorporeal Life Support (ECLS) Protocol Project
Bibliographic record
Abstract
Background: Extracorporeal life support (ECLS) is associated with high morbidity and mortality. Complications and mortality are higher at lower-volume centres. Most Canadian ECLS institutions are low-volume centres. Protocols offer one way to share best practices among institutions to improve outcomes. Whether Canadian centres have ECLS protocols, and whether these protocols are comprehensive and homogenous across centres, is unknown. Methods: Purposeful sampling with mixed methods was used. A Delphi panel defined key elements relevant to the ECLS process. Documentation used in the delivery of ECLS services was requested from programs. Institutional protocols were assessed using deductive coding to determine the presence of key elements. Results: A total of 37 key elements spanning 5 domains (referral, initiation, maintenance, termination, and administration) were identified. Documentation from 13 institutions across 10 provinces was obtained. Institutions with heart or lung transplantation programs had more-complete documentation than did non-transplantation programs. Only 5 key elements were present in at least 50% of protocols (anticoagulation strategy, ventilation strategy, defined referral process, selection criteria, weaning process), and variation was seen in how institutions approached each of these elements. Conclusions: The completeness of ECLS protocols varies across Canada. Programs describe variable approaches to key elements. This variability might represent a lack of evidence or consensus in these areas and creates the opportunity for collaboration among institutions to share protocols and best practice. The key-element framework provides a common language that programs can use to develop ECLS programs, initiate quality-improvement projects, and identify research agendas.
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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 | 0.086 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".