Intravenous Lipid Emulsion as an Antidote for the Treatment of Acute Poisoning: A Bibliometric Analysis of Human and Animal Studies
Bibliographic record
Abstract
In recent years, there has been increasing interest in the role of intravenous lipid formulations as potential antidotes in patients with severe cardiotoxicity caused by drug toxicity. The aim of this study was to conduct a comprehensive bibliometric analysis of all human and animal studies featuring lipid emulsion as an antidote for the treatment of acute poisoning. The Scopus database search was performed on 5 February 2016 to analyse the research output related to intravenous lipid emulsion as an antidote for the treatment of acute poisoning. Research indicators used for analysis included total number of articles, date (year) of publication, total citations, value of the h-index, document types, countries of publication, journal names, collaboration patterns and institutions. A total of 594 articles were retrieved from Scopus database for the period of 1955-2015. The percentage share of global intravenous lipid emulsion research output showed that research output was 85.86% in 2006-2015 with yearly average growth in this field of 51 articles per year. The USA, United Kingdom (UK), France, Canada, New Zealand, Germany, Australia, China, Turkey and Japan accounted for 449 (75.6%) of all the publications. The total number of citations for all documents was 9,333, with an average of 15.7 citations per document. The h-index of the retrieved documents for lipid emulsion research as antidote for the treatment of acute poisoning was 49. The USA and the UK achieved the highest h-indices, 34 and 14, respectively. New Zealand produced the greatest number of documents with international collaboration (51.9%) followed by Australia (50%) and Canada (41.4%) out of the total number of publications for each country. In summary, we found an increase in the number of publications in the field of lipid emulsion after 2006. The results of this study demonstrate that the majority of publications in the field of lipid emulsion were published by high-income countries. Researchers from institutions in the USA led scientific production on lipid emulsion research. There is an obvious need to promote a deeper engagement through international collaborative research projects and funding mechanisms.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.167 | 0.181 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".