A Systematic Review and Meta-analysis Unveiling the Pivotal Role of Extracorporeal Membrane Oxygenation (ECMO) in Drug Overdose Treatment Optimization.
Bibliographic record
Abstract
Objective: The present study aimed to evaluate the clinical benefits and drawbacks of administering ECMO/ECLS therapies to drug-intoxicated patients. Methods: From inception until April 30, 2024, an extensive search was performed on four main databases: PubMed, Web of Science, Cochrane Library, and EMBASE. There was no restriction on the search period. Only the studies that reported survival to hospital discharge rates, adverse events, and the utilization of ECMO/ECLS in the treatment of intoxicated patients were included. On the other hand, articles that did not report adverse events or hospital discharge rates as outcomes, as well as studies published in languages other than English, were excluded. The evaluated outcomes were the rate of survival to hospital discharge rate and the incidence of adverse events associated with ECMO therapy. The Newcastle Ottawa scale was employed to appraise each study to determine its methodological quality. The Comprehensive Meta-Analysis (CMA) software (version 3.0) for statistical analysis was used, with the random effects model (due to high heterogeneity among the studies) and a 95% confidence interval. Results: =70.27%). Conclusion: Despite various health complications, extracorporeal membrane treatment enhanced survival to hospital discharge with good neurological outcomes. Hence, it was a viable, effective, and feasible alternative for managing drug-induced intoxication in patients.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".