Similarity of drug names: Comparison of objective and subjective measures
Bibliographic record
Abstract
Abstract Previous research has shown that objective measures of orthographic (i.e., spelling) similarity can predict the probability of drug‐name confusion, but it is not clear how these objective measures relate to subjective judgments of similarity. This study examined the association between one objective measure of orthographic similarity, the Dice coefficient on trigrams, and one subjective measure, based on the Proscale multidimensional scaling system. Twenty‐seven participants, divided into three groups, performed a similarity grouping task on one of three sets of 70 drug names drawn at random from a larger set of similar and dissimilar name pairs. Subjective groupings were converted to dissimilarity scores with the use of the Proscale multidimensional scaling program. The association between subjective and objective measures was assessed by correlation and regression analyses. Correlations between subjective and objective measures were −0.70, −0.48, and −0.53 for the three groups, respectively (p < .001). Regression models with trigram similarity as the main predictor accounted for between 22 and 48% of the variance in subjective dissimilarity scores. It is concluded that objective measures of orthographic similarity between drug names are valid but incomplete measures of subjective similarity. © 2002 Wiley Periodicals, Inc.
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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 | 0.005 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".