Methodology for AACT evidence-based recommendations on the use of intravenous lipid emulsion therapy in poisoning
Bibliographic record
Abstract
Intravenous lipid emulsion (ILE) therapy is a novel treatment that was discovered in the last decade. Despite unclear understanding of its mechanisms of action, numerous and diverse publications attested to its clinical use. However, current evidence supporting its use is unclear and recommendations are inconsistent. To assist clinicians in decision-making, the American Academy of Clinical Toxicology created a workgroup composed of international experts from various clinical specialties, which includes representatives of major clinical toxicology associations. Rigorous methodology using the Appraisal of Guidelines for Research and Evaluation or AGREE II instrument was developed to provide a framework for the systematic reviews for this project and to formulate evidence-based recommendations on the use of ILE in poisoning. Systematic reviews on the efficacy of ILE in local anesthetic toxicity and non-local anesthetic poisonings as well as adverse effects of ILE are planned. A comprehensive review of lipid analytical interferences and a survey of ILE costs will be developed. The evidence will be appraised using the GRADE system. A thorough and transparent process for consensus statements will be performed to provide recommendations, using a modified Delphi method with two rounds of voting. This process will allow for the production of useful practice recommendations for this therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.154 | 0.450 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.048 | 0.036 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.112 | 0.018 |
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".