Predictors associated with successful weaning of veno-venous extracorporeal membrane oxygenation and mortality in adult patients with severe acute lung failure: Protocol of a pooled data analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Severe acute lung failure (ALF) often necessitates veno-venous extracorporeal membrane oxygenation (VV-ECMO), where identifying predictors of weaning success and mortality remains crucial yet challenging. The study aims to identify predictors of weaning success and mortality in adults undergoing VV-ECMO for severe ALF, a gap in current clinical knowledge. METHODS AND ANALYSIS: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials will be searched for cohort studies examining the predictive factors of successful weaning and mortality in adult patients on VV-ECMO due to severe ALF. Risk of bias assessment will be conducted using the Newcastle-Ottawa scale for each included study. The primary outcomes will be successful weaning from VV-ECMO and all-cause mortality. Between-study heterogeneity will be evaluated using the I2 statistic. Sensitivity, subgroup, and meta-regression analyses will be performed to ascertain potential sources of heterogeneity and assess the robustness of our results. We will use the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool to recommend the level of evidence. DISCUSSION: This study seeks to provide clinically significant insights into predictors for weaning and mortality during VV-ECMO treatment for ALF, aiming to support clinical decisions and potentially influence health policy, thereby improving patient outcomes. ETHICS AND DISSEMINATION: Given the absence of direct engagement with human subjects or access to personal medical records, ethical approval for this study is deemed unnecessary. The study findings will be shared at a scientific conference either at the global or national level. Alternatively, the results will be presented for publication in a rigorously peer-reviewed journal regarding critical care medicine.
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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 | 0.126 | 0.156 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.045 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".