Trends in Domestic Animal Medico‐Legal Pathology Cases Submitted to a Veterinary Diagnostic Laboratory 1998–2010*
Bibliographic record
Abstract
Pathologists at veterinary diagnostic laboratories receive medico-legal cases from a variety of animal species for postmortem examination. A search of computerized records of the Animal Health Laboratory, University of Guelph, Guelph, Ontario, Canada from 1998 to 2010 identified 1706 medicolegal cases. These were categorized according to the history as criminal investigations, anesthetic-related deaths, insurance, litigation, malpractice cases, and regulatory cases. Statistically significant linear trends in the proportion of medicolegal cases for all animals and criminal cases for companion animals were identified over the 12 year period. Companion animals had significantly greater odds of being a medicolegal case in all categories except for insurance and regulatory cases, compared to noncompanion animals. Based on pathology reports for the 271 criminal cases, 43.1% were consistent with neglect, 29.2% were compatible with non-accidental injury, 4.80% were poisonings, 10.7% were deemed to be due to natural disease, and 11.43% were inconclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".