Audit on the Use of Dangerous Abbreviations, Symbols, and Dose Designations in Paper Compared to Electronic Medication Orders: A Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Dangerous abbreviations on the Institute for Safe Medication Practices Canada's "Do Not Use" list have resulted in medication errors leading to harm. Data comparing rates of use of dangerous abbreviations in paper and electronic medication orders are limited. OBJECTIVE: To compare rates of use of dangerous abbreviations from the "Do Not Use" list, in paper and electronic medication orders. Secondary objectives include determining the proportion of patients at risk for medication errors due to dangerous abbreviations and the most commonly used dangerous abbreviations. METHODS: test. The proportion of patients with at least 1 medication order containing dangerous abbreviation(s) and the top 5 dangerous abbreviations used were described. RESULTS: Overall, 255 patient charts were reviewed. The proportions of paper and electronic medication orders containing dangerous abbreviation(s) were 172/714 (24.1%) and 9/2207 (0.4%), respectively ( P < 0.001). Almost one-third of patients had medication order(s) containing dangerous abbreviation(s). The proportions of patients with at least 1 medication order during the audit period containing dangerous abbreviation(s) for patients with paper only, electronic only, or a hybrid of paper and electronic medication orders were 50.5%, 5%, and 47.2%, respectively. Those most commonly used were "D/C", drug name abbreviations, "OD," "cc," and "U." CONCLUSIONS: Electronic medication orders have significantly lower rates of dangerous abbreviation use compared to paper medication orders.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".