A comparison of two user-friendly methods to identify and support correction of misspelled medications
Bibliographic record
Abstract
Objective: To identify and support correction of misspelled medication names recorded as free text, we compared the relative effectiveness of two user-friendly methods, used without reliance on clinical knowledge. Methods: Leveraging the SAS® COMPGED function, fuzzy string search programs examined 1.8 million medication records from 183,600 World Trade Center General Responder Cohort monitoring visits conducted in New York and New Jersey between 7/16/2002 and 3/31/2021, producing replicable generalized edit distance scores between the reported and correct spelling. Scores < 120 were selected as optimal and compared to Stedman's 2020 Plus Medical/Pharmaceutical Spell Checker first suggested word, used as the comparative standard because it employs both spelling and phonetic similarities to suggest matching words. We coded each methods' results as identifying or not identifying the medications within each visit. Results: Most types of medications (94.4 % anxiety, 98.4 % asthma and 94.6 % ulcer/gastroesophageal reflux disease) were correctly spelled. Cross tabulations assessed the agreement (anxiety 99.9 %, asthma 99.6 % and 98.4 % ulcer/ gastroesophageal reflux disease), false positive (respectively 0.02 %, 0.03 % and 2.0 %) and false negative (respectively 1.9 %, 0.5 % and 1.0 %) values. Scores < 120 occasionally correctly identified medications missed by the spell checker. We observed no difference in medication misspellings across socio-economically and culturally diverse patient characteristics. Conclusions: Both methods efficiently identified most misspelled medications, greatly minimizing the review and rectification needed. The fuzzy method is more universally applicable for condition-specific medications identification, but requires more programming skills. The spell checker is inexpensive, but benefits from modest programming skills and is only available in some languages.
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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 | 0.022 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".