The Pathology of Perianesthetic Death in Dogs and Cats
Bibliographic record
Abstract
Perianesthetic death (PAD) in veterinary medicine is a relatively infrequent but significant event, with a consistently higher reported incidence across all veterinary species compared to humans. While numerous clinical studies have explored the causes and risk factors associated with PAD, few have focused on the postmortem findings of these cases. This thesis examines the pathology of PAD in dogs and cats through a retrospective review of postmortem submissions from multiple Canadian veterinary diagnostic laboratories. The first component of this research analyzed postmortem reports from four Canadian diagnostic laboratories, examining tendencies in patient demographics and pathological findings. A notable proportion of submissions involved dogs and cats undergoing elective spay/neuter procedures, most of which were classified as healthy (low American Society of Anesthesiologists (ASA) physical status classification) prior to anesthesia with many of these animals lacking definitive postmortem lesions. Furthermore, submission forms frequently lacked important clinical details, presenting challenges for pathologists in determining the underlying cause of PAD. These findings are similar to previous studies and identify the difficulties in diagnosing PAD-related fatalities and the limitations of postmortem evaluations. The second component of this research examined trends in feline PAD by comparing postmortem submissions from general practices and a referral teaching hospital. After excluding cases with known pre-existing clinical disease, cause-of-death determinations were similar between practice types. Additionally, inconsistencies in pathology report documentation were observed. To address these gaps, a standardized submission form and postmortem checklist were developed with the goal to help improve diagnostic accuracy. This research builds upon the limited existing literature on PAD in companion animals and identifies the need for a more standardized approach to the investigation of PAD postmortem examination. Future work should focus on obtaining a thorough clinical history, implementing a standardized postmortem protocol and collaboration with other veterinary disciplines including veterinary anesthesiologists. The ultimate goal is to advance the understanding of PAD in veterinary medicine leading to decreased deaths and overall improving patient safety.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".