Lipid emulsions in the treatment of acute poisoning: a systematic review of human and animal studies
Bibliographic record
Abstract
OBJECTIVE: To assess the evidence regarding the efficacy and safety of intravenous fat emulsion (IFE) in the management of poisoned patients. METHODS: We performed a systematic review of the literature with no time or language restriction. The electronic databases were searched from their inception until June 1, 2009 (Medline, EMBASE, ISI web of science, Biological abstract, LILACS, ChemIndex, Toxnet, and Proquest). We also examined the references of identified articles and the gray literature. The target interventions eligible for inclusion were administration of any IFE before, during, or after poisoning in human or animals. All types of studies were reviewed. Eligibility for inclusion and study quality scores, based on criteria by Jadad and the STROBE statement, were evaluated by independent investigators. The primary outcome was mortality. Secondary outcomes included neurologic, hemodynamic, and electrocardiographic variables, as well as adverse effects. RESULTS: Of the 938 publications identified by the search strategies, 74 met the inclusion criteria. We identified 23 animal trials, 50 human, and 1 animal case reports. Overall, the quality of evidence was weak and significant heterogeneity prevented data pooling. Available data suggest some benefits of IFE in bupivacaine, verapamil, chlorpromazine, and some tricyclic antidepressants and beta-blockers toxicity. No trial assessed the safety of IFE in the treatment of acute poisoning. CONCLUSION: The evidence for the efficacy of IFE in reducing mortality and improving hemodynamic, electrocardiographic, and neurological parameters in the poisoned patients is solely based on animal studies and human case reports. The safety of IFE has not been established.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".