Accuracy and usability of medication identifiers for solid oral medications
Bibliographic record
Abstract
Background A comprehensive, contemporary evaluation of medication identifiers is necessary to keep up with the fast‐paced mobile and web‐based technology used by health care professionals and patients in order to safely identify and use oral medications. Prior studies evaluating the accuracy of medication identifiers are dated, with the most recent solely examining imprints of oral medications. Objective To compare the accuracy of different medication identifiers, and to identify and quantify ease of use between lay and professional medication identifiers. Methods We conducted a cross‐sectional study of 202 randomly selected oral medications, comparing the results of 14 lay and professional medication identifiers with reference standard‐identified medications. Investigators conducted three different searches for each medication using a standardized search methodology, including each medication's imprint, shape, color, scoring, and dosage form. Results Ident‐A‐Drug, Drugs.com , Facts & Comparisons, and web‐based Lexicomp were the four most accurate identifiers at 98%, 97.5%, 96.5%, and 96.5%, respectively. Web‐based identifiers correctly identified more medications compared with mobile‐based identifiers (93.2% vs 80.6%, P <0.001). Drugs.com displayed the medication as the first result most often (96%), followed by Facts & Comparisons (95%). Drugs.com found the medication on the first search most frequently (97%). Searches without color were more accurate than with color ( P <0.001). The most user‐friendly identifiers were Facts & Comparisons, Drugs.com , Epocrates Mobile, and Lexicomp Mobile. Conclusion Drugs.com , Facts & Comparisons, and Lexicomp (web and mobile) were determined to be the most accurate and easy‐to‐use medication identifiers. Searching without color was more accurate than searching with color.
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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 | 0.024 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".