Bibliographic record
Abstract
Autopsy of animals that die in the perianesthetic period allows identification of anesthetic and surgical complications as well as preexisting disease conditions that may have contributed to mortality. In most studies to date investigating perianesthetic mortality in animals, inclusion of autopsy data is very limited. This retrospective study evaluated autopsy findings in 221 cases of perianesthetic death submitted to a veterinary diagnostic laboratory from primary care and referral hospitals. Canine (n = 105; 48%) and feline (n = 90; 41%) cases predominated in the study, involving elective (71%) and emergency (19%) procedures. The clinical history provided to the pathologist was considered incomplete in 42 of 221 cases (19%), but this history was considered essential for evaluating the circumstances of perianesthetic death. Disease had been recognized clinically in 69 of 221 animals (31%). Death occurred in the premedication or sedation (n = 19; 9%), induction (n = 22; 11%), or maintenance (n = 73; 35%) phases or in the 24 hours postanesthesia (n = 93 animals; 45%). Lesions indicative of significant natural disease were present in 130 of 221 animals (59%), mainly involving the heart, upper respiratory tract, or lungs. Surgical or anesthesia-associated complications were identified in 10 of 221 cases (5%). No lesions were evident in 80 of 221 animals (36%), the majority of which were young, healthy, and undergoing elective surgical procedures. Lesions resulting from cardiopulmonary resuscitation were identified in 75 of 221 animals (34%). Investigation of perianesthetic death cases should be done with knowledge of prior clinical findings and antemortem surgical and medical procedures; the autopsy should particularly focus on the cardiovascular and respiratory system, including techniques to identify pneumothorax and venous air embolism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".