A Multicenter International Survey of Renal Supportive Therapy During ECMO
Bibliographic record
Abstract
Acute kidney injury and fluid overload (FO) are associated with increased mortality in critically ill patients, including the subset supported with extracorporeal membrane oxygenation (ECMO). The indication for and method of application of renal support therapy (RST) during ECMO is largely unknown beyond single-center experiences. The current study uses a survey design to document practice variation regarding RST, including indication, method of interface with the ECMO circuit, and prescribing practices. Sixty-five international ECMO centers (31%) responded to an online electronic survey regarding RST during ECMO. Nearly a quarter of centers (23%) reported using no RST during ECMO. Among those using the therapy, the predominant mode of therapy applied was convection and included slow continuous ultrafiltration and continuous venovenous hemofiltration. The predominant indication for RST was the treatment (43%) or prevention (16%) of FO. Nephrology rather than critical care medicine is reported as the prescribing service in a majority of centers with a significant difference between US centers and non-US centers. The results of this study identify a wide variation in practice regarding RST during ECMO that will offer multiple important avenues for further research by this group and others regarding the interface of RST and ECMO.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".