Innovating for Curriculum Design Using a Text-Mining Exploration of Common Clinical Topics in Equine Primary Care
Bibliographic record
Abstract
To ensure veterinary students are prepared for clinical practice, curricula must provide opportunities for students to learn about the cases most frequently seen in practice. Currently, there is a gap in the literature with regard to the identification of common clinical topics encountered in equine primary care practice in the UK. This study aims to address this gap by utilizing text-mining techniques on electronic medical records (EMRs) to produce a ranked list of the most common clinical topics encountered in equine primary care in the UK. The study included 1,092,731 rows of data from 150,465 equine patients between 2012 and 2022, sourced from six primary care equine practices. Dictionaries were developed in the text-mining software and applied to the corpus of EMRs, enabling the identification of 30 common clinical topics. The clinical topics were ranked in order of their prevalence, and estimates of incidence rate per horse-year calculated for each. Results showed lameness, vaccination, sedation, dental, and worming as the five most frequently mentioned clinical topics in equine primary care EMRs in the UK. This work provides an evidence-based list of commonly encountered clinical topics in equine primary care practice, guiding educators to focus their teaching and curricula, and students to prioritize their learning. The results of this study provide data-driven validation of core concepts that should be prioritized within equine undergraduate curricula.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".