Extracorporeal life support in trauma: Indications and techniques
Bibliographic record
Abstract
BACKGROUND: Clarity about indications and techniques in extracorporeal life support (ECLS) in trauma is essential for timely and effective deployment, and to ensure good stewardship of an important resource. Extracorporeal life support deployments in a tertiary trauma center were reviewed to understand the indications, strategies, and tactics of ECLS in trauma. METHODS: The provincial trauma registry was used to identify patients who received ECLS at a Level I trauma center and ECLS organization-accredited site between January 2014 and February 2021. Charts were reviewed for indications, technical factors, and outcomes following ECLS deployment. Based on this data, consensus around indications and techniques for ECLS in trauma was reached and refined by a multidisciplinary team discussion. RESULTS: A total of 25 patients underwent ECLS as part of a comprehensive trauma resuscitation strategy. Eighteen patients underwent venovenous ECLS and seven received venoarterial ECLS. Nineteen patients survived the ECLS run, of which 15 survived to discharge. Four patients developed vascular injuries secondary to cannula insertion while four patients developed circuit clots. On multidisciplinary consensus, three broad indications for ECLS and their respective techniques were described: gas exchange for lung injury, extended damage control for severe injuries associated with the lethal triad, and circulatory support for cardiogenic shock or hypothermia. CONCLUSION: The three broad indications for ECLS in trauma (gas exchange, extended damage control and circulatory support) require specific advanced planning and standardization of corresponding techniques (cannulation, circuit configuration, anticoagulation, and duration). When appropriately and effectively integrated into the trauma response, ECLS can extend the damage control paradigm to enable the management of complex multisystem injuries. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".