Evaluation of Readability Scores of Discharge Summaries Written by Senior Veterinary Students and Clinical Instructors on a Primary Care Rotation
Bibliographic record
Abstract
Health literacy, the ability of patients to understand and make informed decisions regarding health care, is an integral determinant of compliance and satisfaction with care. Readability of patient-oriented materials is a major factor affecting health literacy. As a result, the National Institutes of Health (NIH) and the American Medical Association (AMA) have published recommendations that public-oriented health education materials be written at a maximum of a sixth-grade equivalent reading level. Animal caregivers' health literacy likely has a similarly significant impact on animal care and welfare. Current literature, however, shows that many veterinary education materials are written at higher than recommended levels. Educating veterinary students in the utility and methodology of creating readable medical discharge documents is paramount to addressing this issue. In this study, student-created discharge documents and clinical instructor edits were collected from medical record software at the beginning and conclusion of the clinical year. Discharge documents were categorized by the patient's reason for presentation (wellness, non-wellness, or procedural) as well as whether the recipient had advanced veterinary training. Using a readability calculator, the Flesch-Kincaid (F-K) readability score was reported for each document. Mean readability scores for both groups exceeded the recommended sixth-grade reading level. No significant difference was found between student and instructor scores for all document types at both time points. Wellness documents had the highest mean readability scores, and procedural documents had the lowest. Student scores collectively increased at the second time point, with non-wellness documents exhibiting the largest degree of disparity between the two time points. The recipient's degree of veterinary training did not significantly affect readability scores.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".