Evaluation of Retention of Veterinary Clinical Pathology Knowledge between Second-Year and Fourth-Year Clinical Pathology Courses
Bibliographic record
Abstract
There is a concern over long-term retention of knowledge in professional programs. The goal of this study was to evaluate the retention of veterinary clinical pathology knowledge between the fourth-semester and fourth-year clinical pathology courses. We hypothesize that students will forget a significant amount of content area knowledge between the fourth semester and fourth year in the Doctor of Veterinary Medicine (DVM) program. We further hypothesize that a review of material during the fourth-year clinical pathology rotation will help students rebuild existing knowledge and increase performance on specific test questions, between T2 (rotation pre-test) and T3 (rotation post-test). Initial mastery of course material was assessed via a 94-item multiple-choice final exam (T1) given in the semester 4 clinical pathology course. Retention of course material from semester 4 to year 4 was assessed via a 55-item multiple-choice pre-test, administered at the start of the clinical pathology rotation in year 4 while learning/mastery during the clinical rotation was assessed via a 55-item multiple-choice post-test, administered at the end of each clinical pathology rotation. In this study, evidence of knowledge retention between semester 4 and year 4 was 55.5%. There is a small increase in the measure of knowledge gain from the beginning to the end of the rotation. As an added benefit, we were able to use identified trends for retention of knowledge within specific subject areas as a mechanism to evaluate the effectiveness of our course and reallocate additional instructional time to topics with poorer retention.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".