The ELSO Maastricht Treaty for ECLS Nomenclature: abbreviations for cannulation configuration in extracorporeal life support - a position paper of the Extracorporeal Life Support Organization
Bibliographic record
Abstract
BACKGROUND: The Extracorporeal Life Support Organization (ELSO) Maastricht Treaty for Nomenclature in Extracorporeal Life Support (ECLS) established consensus nomenclature and abbreviations for ECLS to ensure accurate, concise communication. METHODS: We build on this consensus nomenclature by layering a framework of precise and efficient abbreviations for cannula configuration that describe flow direction, number of cannulae used, any additional ECLS-related catheters, and cannulation sites. This work is a consensus of international representatives of the ELSO, including those from the North American, Latin American, European, South and West Asian, and Asian-Pacific chapters of ELSO. RESULTS: The classification increases in descriptive capability by introducing a third (cannula tip position) and fourth (cannula dimension) level to those provided in the previous consensus on ECLS cannulation configuration nomenclature. This expansion offers the simplest level needed to convey cannulation information yet allows for more details when required. CONCLUSIONS: A complete nomenclature for ECLS cannulation configurations accommodating future revisions was developed to facilitate ability to compare practices and results, to promote efficient communication, and to improve quality of registry data.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".