Responding to the challenge of look-alike, sound-alike drug names
Bibliographic record
Abstract
Despite significant advances in medication safety, errors related to confusion between drug names are a cause of preventable adverse events and serious harm,1 and remain a patient safety priority.2 ,3 Although drug name confusion is recognised as a factor contributing to error, its minimisation or elimination is a prevailing challenge.4 ,5 In this issue, Schroeder et al 6 postulate that despite industry's efforts to follow regulators' guidance7 on how to review drug names, more objective evidence, in a standardised format, is needed to improve decision-making about the acceptability of a name. To address this concern, the authors assessed the association between error rates in laboratory-based tests of drug name memory and perception and rates of real-world errors related to drug name confusion. We commend the authors for their contribution to this important area of study. Results from a study of a postmarket strategy for preventing drug name errors (ie, Tallman lettering) with look-alike, sound-alike (LASA) drug names did not demonstrate effectiveness in reducing medication errors.8 ,9 Reliable strategies for preventing drug name confusion errors, before they reach the market, are needed. The authors present a validated approach that provides an opportunity for identifying confusing drug names during the premarket phase with the goal of identifying safer names for products and preventing the associated costs when LASA drug names are identified postmarket. Here, we comment briefly on the article with the aim of provoking further reflection upon some of the fundamental issues surrounding assessment of LASA drug names and to protect patients from potentially harmful medication errors. The findings of the thoughtfully executed study by Schroeder et al represent an important contribution to knowledge about the …
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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 | 0.050 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".