A Survey of Attitudes of Board-Certified Veterinary Pathologists to Forensic Veterinary Pathology
Bibliographic record
Abstract
An electronic survey was conducted to determine the attitudes of veterinary pathologists toward forensic pathology and the adequacy of their training in the discipline. The survey was sent to 1933 diplomates of the American College of Veterinary Pathologists and 311 completed responses were analyzed. Of respondents, 80% report receiving at least 1 type of medicolegal case, with cases from law enforcement received most frequently. Most (74%) of the respondents indicated that their previous training did not prepare them adequately to handle forensic cases and almost half of the respondents (48%) indicated that they needed more training on serving as an expert witness. Relative risk ratios (RRR) and odds ratios (OR) were generated to determine the strength of a statistically significant association. Responses from a free-text entry question determining additional training needs could be grouped into 3 main categories: (1) veterinary forensic pathology science and procedures, (2) documentation, evidence collection and handling, and (3) knowledge of the medicolegal system. Last, a field for additional comments or suggestions regarding veterinary forensic pathology was completed by 107 respondents and many reinforced the need for training in the categories previously described. The survey highlights that a significant proportion of diplomates of the American College of Veterinary Pathologists are currently engaged in veterinary forensic pathology but feel their training has not adequately prepared them for these cases. Hopefully, the survey results will inform the college and residency training coordinators as they address the training requirements for an important emerging discipline.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".