Examining patterns in medication documentation of trade and generic names in an academic family practice training centre
Bibliographic record
Abstract
BACKGROUND: Studies in the United States have shown that physicians commonly use brand names when documenting medications in an outpatient setting. However, the prevalence of prescribing and documenting brand name medication has not been assessed in a clinical teaching environment. The purpose of this study was to describe the use of generic versus brand names for a select number of pharmaceutical products in clinical documentation in a large, urban academic family practice centre. METHODS: A retrospective chart review of the electronic medical records of the St. Michael's Hospital Academic Family Health Team (SMHAFHT). Data for twenty commonly prescribed medications were collected from the Cumulative Patient Profile as of August 1, 2014. Each medication name was classified as generic or trade. Associations between documentation patterns and physician characteristics were assessed. RESULTS: Among 9763 patients prescribed any of the twenty medications of interest, 45% of patient charts contained trade nomenclature exclusively. 32% of charts contained only generic nomenclature, and 23% contained a mix of generic and trade nomenclature. There was large variation in use of generic nomenclature amongst physicians, ranging from 19% to 93%. CONCLUSIONS: Trade names in clinical documentation, which likely reflect prescribing habits, continue to be used abundantly in the academic setting. This may become part of the informal curriculum, potentially facilitating undue bias in trainees. Further study is needed to determine characteristics which influence use of generic or trade nomenclature and the impact of this trend on trainees' clinical knowledge and decision-making.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".