Feasibility of using tissue autolysis to estimate the postmortem interval in horses
Bibliographic record
Abstract
Estimation of the postmortem interval (PMI) is a poorly studied field in veterinary pathology. The development of field-applicable methods is needed given that animal cruelty investigations are increasing continually. We evaluated various histologic criteria in equine brain, liver, and muscle tissue to aid the estimation of PMI in horses, which is central to forensic investigations of suspicious death. After death, autolysis proceeds predictably, depending on environmental conditions. Currently, no field-applied methods exist that accurately estimate the PMI using histology in animals or humans through quantification of autolysis. Brain, liver, and skeletal muscle from 12 freshly euthanized horses were held at 22°C and 8°C for 72 h. Tissues were sampled at T0h, T1h, T2h, T4h, T6h, T12h, T24h, T36h, T48h, T60h, and T72h. For each tissue, we quantified 5 to 7 criteria associated with autolysis, based on the percentage of microscopic field involved. Each criterion was modeled, with temperature and time as independent variables. Changes were most predictable in liver and muscle over the first 72 h postmortem. The criteria for autolysis that were present most extensively at both temperatures were hepatocyte individualization and the separation of bile duct epithelium from the basement membrane. The changes that were present next most extensively were disruption of myofiber continuity, hypereosinophilia, and loss of striation. Brain changes were highly variable. The high statistical correlation between the parameter "autolysis" and the variables "time/temperature", indicates that autolysis is progressive and predictable. Further investigation of these criteria is needed to establish histologic algorithms for PMI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".