Guidelines for resident training in veterinary clinical pathology. III: cytopathology and surgical pathology
Bibliographic record
Abstract
The Education Committee of the American Society for Veterinary Clinical Pathology has identified a need for improved structure and guidance of training residents in clinical pathology. This article is the third in a series of articles that address this need. The goals of this article are to describe learning objectives and competencies in knowledge, abilities, and skills in cytopathology and surgical pathology (CSP); provide options and ideas for training activities; and identify resources in veterinary CSP for faculty, training program coordinators, and residents. Guidelines were developed in consultation with Education Committee members and peer experts and with evaluation of the literature. The primary objectives of training in CSP are: (1) to develop a thorough, extensive, and relevant knowledge base of biomedical and clinical sciences applicable to the practice of CSP in domestic animals, laboratory animals, and other nondomestic animal species; (2) to be able to reason, think critically, investigate, use scientific evidence, and communicate effectively when making diagnoses and consulting and to improve and advance the practice of pathology; and (3) to acquire selected technical skills used in CSP and pathology laboratory management. These guidelines define expected competencies that will help ensure proficiency, leadership, and the advancement of knowledge in veterinary CSP and will provide a useful framework for didactic and clinical activities in resident-training programs.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.019 |
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".