A Systematic Review of Ethanol and Fomepizole Use in Toxic Alcohol Ingestions
Bibliographic record
Abstract
Objectives. The optimal antidote for the treatment of ethylene glycol or methanol intoxication is not known. The objective of this systematic review is to describe all available data on the use of ethanol and fomepizole for methanol and ethylene glycol intoxication. Data Source. A systematic search of MEDLINE and EMBASE was conducted. Study Selection. Published studies involving the use of ethanol or fomepizole, or both, in adults who presented within 72 hours of toxic alcohol ingestion were included. Our search yielded a total of 145 studies for our analysis. There were no randomized controlled trials, and no head-to-head trials. Data Extraction. Variables were evaluated for all publications by one independent author using a standardized data collection form. Data Synthesis. 897 patients with toxic alcohol ingestion were identified. 720 (80.3%) were treated with ethanol (505 Me, 215 EG), 146 (16.3%) with fomepizole (81 Me, 65 EG), and 33 (3.7%) with both antidotes (18 Me, 15 EG). Mortality in patients treated with ethanol was 21.8% for Me and 18.1% for EG. In those administered fomepizole, mortality was 17.1% for Me and 4.1% for EG. Adverse events were uncommon. Conclusion. The data supporting the use of one antidote is inconclusive. Further investigation is warranted.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| 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".