American Society for Veterinary Clinical Pathology–Recommended Clinical Pathology Competencies for Graduating Veterinarians
Bibliographic record
Abstract
Given the move toward competency-based veterinary education and the subsequent reevaluation of veterinary curricula, there is a need for specialties to provide guidance to veterinary college administrators and educators on the core knowledge and skills pertaining to their specialty to ensure their inclusion in revised or redesigned curricula. The American Society for Veterinary Clinical Pathology (ASVCP) Education Committee sought to create a list of competencies specific to clinical pathology expected of graduating veterinarians. The stimulus for this project was the American Veterinary Medical Association Council on Education Standards of Accreditation for Colleges of Veterinary Medicine, further driven by the 2018 publication of the Association of American Veterinary Medical Colleges Competency-Based Veterinary Education Working Group framework. The recommendations made in this document are the culmination of the 2016 ASVCP Education Forum for Discussion, multiple remote subcommittee communications, and feedback obtained from ASVCP membership. The final framework includes 8 clinical pathology-focused domains of competence with 20 clinical pathology competencies and 61 clinical pathology illustrative sub-competencies. The clinical pathology-focused domains of competence are: the pre-analytical phase of testing, laboratory medicine and instrumentation, principles of test selection and interpretation, hematology and hemostasis, chemistry, endocrinology, urinalysis, and cytology. These are not intended to replace the nine established AAVMC domains of competence with supportive competencies and illustrative sub-competencies but to guide institutions for how clinical pathology aligns within the competency-based veterinary education (CBVE) framework for the practice-ready veterinary graduate. This clinical pathology competency framework may prove useful and empowering during discussions of curriculum revisions and redesigns.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".