Bibliographic record
Abstract
Asphyxia in a forensic context refers to death by rapid cerebral anoxia or hypoxia due to accidental or nonaccidental injury. Death due to nondrowning asphyxia can occur with strangulation, suffocation, and mechanical asphyxia, each of which is categorized based on the mechanism of injury. Individuals dying due to various types of asphyxia may or may not have lesions, and even those lesions that are present may be due to other causes. The interpretation or opinion that death was due to asphyxia requires definitive and compelling evidence from the postmortem examination, death scene, and/or history. Beyond the postmortem examination, pathologists may be faced with questions of forensic importance that revolve around the behavioral and physiological responses in animals subjected to strangulation, suffocation, or mechanical asphyxia to determine if the animal suffered. While there is no prescriptive answer to these questions, it is apparent that, because of physiological and anatomical differences between humans and animals, for some mechanisms of asphyxia, consciousness is maintained for longer periods and the onset of death is later in animals than that described for people. Veterinary pathologists must be cognizant that direct extrapolation from the medical forensic literature to animals may be incorrect. This article reviews the terminology, classification, mechanisms, and lesions associated with asphyxial deaths in companion animals and highlights significant comparative differences of the response to various types of asphyxia in animals and people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".