Improving the identification of confused drug names in Spanish
Why this work is in the frame
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Bibliographic record
Abstract
Since a drug name goes through different communication means and circumstances when it is prescribed, written, advertised, listened to, searched and administered; it tends to be confused with similar drug names that Look-Alike and Sound-Alike (LASA). LASA drug names have caused costs and damage to health. For this problem, the institutions of the United Kingdom, Canada, and the United States have implemented programs for several decades to report lists of confusing drug names pairs. Thanks to these kinds of list, it has been possible to propose new models to identify confusing drug names in English and are used to reject new drug name proposals or to alert when a confusing drug name is being dispensed. However, countries such as Spain also have published a list with the Spanish LASA drug names, and it is not clear enough whether the models previously proposed for the drug names in English are useful for the list in Spanish or if it is necessary to adjust and update them for the Spanish language. This paper focuses on updating and improving the identification of LASA drug names in Spanish. First, we update the state-of-the-art by evaluating all the individual similarity measures proposed previously and all the models that combine these measures with the list in Spanish. Second, we updated the models with new individual measures and then adjusted them with the list in Spanish to improve the identification of LASA drug names in Spanish. After that, 25 individual similarity measures and 8 models to identify confused drug names in Spanish are compared to obtain the best result and conclusions.
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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 | 0.001 |
| 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 | 0.000 |
| 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 it