Residency Training Programs in Veterinary Clinical Pathology: A Comparison of Experiences at Two Institutions
Bibliographic record
Abstract
Two institutions with different residency training formats in clinical pathology are compared with respect to application procedures, learning and teaching opportunities, learning resources, research training, publication requirements, and assessment methods of the program and trainees. The University of Florida and Purdue University programs are both based on an emphasis in morphologic recognition and interpretation of disease processes as well as training in basic science and applied research principles. The progress of trainees through each program is carefully monitored to meet individual needs as well as to meet the training requirements to allow candidates to sit for the certifying examination in clinical pathology. Periodic mock board exams are a critical tool to assess trainee progress and learning. The differences in format focus on coursework and publication requirements as well as on program assessment tools. While one program provides training in the form of 75% clinical diagnostic service, the other uses a mixture of 50% coursework and 50% clinical diagnostic training. Despite the contrast between a pure residency training program and one combining residency training with an MS degree, both institutions provide a solid program structure, ample learning resources, and adequate faculty mentorship to produce a high pass rate of board-certified specialists, the major focus for both programs. Numbers of post-training employment positions for both institutions are similar for those selecting faculty positions at veterinary schools. During the period studied, however, the combined residency and MS graduate program at Purdue University produced more graduates employed in pharmaceutical and biotechnology companies, while the residency program at the University of Florida produced more graduates employed by diagnostic laboratories.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".