Medication errors associated with the use of ethanol and fomepizole as antidotes for methanol and ethylene glycol poisoning
Bibliographic record
Abstract
INTRODUCTION: Little is known about medication errors which occur with the antidotes ethanol and fomepizole, used for treatment of methanol and ethylene glycol poisoning. Study objectives were to describe and compare the frequency, type, outcome and underlying causes of medication errors associated with ethanol and fomepizole. METHODS: Patients aged ≥13 years were included if they were hospitalized in 1996-2005 for methanol or ethylene glycol poisoning and treated with ethanol or fomepizole. Charts from 10 hospitals were separately reviewed by two abstracters who recorded case details. A consensus panel of clinicians used the abstracted data to identify medication errors and classify error outcome. Fisher's exact test determined significant differences in the proportion of ethanol and fomepizole-treated cases with medication error and univariate logistic regression identified risk factors associated with harmful dosage errors. RESULTS: There were 145 ethanol- and 44 fomepizole-treated cases. There was ≥1 medication error in 113/145 (78%) ethanol- and 20/44 (45%) fomepizole-treated cases (p = 0.0001) with more ethanol-related errors involving excessive dose, inadequate monitoring and inappropriate antidote duration. Harmful errors occurred in 19% of ethanol- and 7% of fomepizole-treated cases (p = 0.06) and were largely due to excessive antidote dose or delayed antidote initiation. Occurrence of harmful dosage error was reduced in cases with Poison Control Centre consultation, odds ratio (95% confidence interval) 0.39 (0.17, 0.91), hemodialysis 0.37 (0.16, 0.88), or fomepizole versus ethanol 0.24 (0.06, 1.04). CONCLUSION: Fomepizole was less prone to medication error than ethanol. Error-related harm was most commonly due to excessive antidote dose or delayed antidote initiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".