Impact of carotid artery sectioning on cerebral blood flow in bovines: a numerical hemodynamic study
Bibliographic record
Abstract
Objective: To investigate the hemodynamic consequences of carotid sectioning, as performed in religious slaughter, on cerebral blood flow (CBF) and pressure in cattle. Religious slaughter typically does not allow preslaughter stunning, which has led to scrutiny over concerns that blood flow from intact vertebral arteries or vascular occlusions could delay loss of consciousness and potentially result in unnecessary pain and suffering. Methods: A numerical model of the bovine cerebrovascular system was developed using COMSOL Multiphysics, incorporating anatomical and physiological parameters. Simulations were conducted across pre, immediate, and postcarotid sectioning states and scenarios involving vessel occlusions. Results: The simulation revealed a near-instantaneous loss of approximately 99% of CBF and pressure immediately following carotid sectioning. Vertebral artery flow is redirected away from the brain, via the vertebral-occipital anastomosis, toward the severed carotids. Simulated carotid occlusions did not meaningfully alter cerebral hemodynamics or delay the loss of blood flow and pressure. Conclusions: Carotid sectioning causes an almost complete and immediate cessation of cerebral perfusion, with vertebral artery flow and carotid occlusions exerting negligible influence. The results support the conclusion that religious slaughter methods, such as shechita and halal, in which the major blood vessels are severed, induce rapid loss of CBF and pressure and therefore rapid loss of consciousness. Clinical Relevance: This work underscores the value of numerical modeling in providing objective insights into the hemodynamics of religious slaughter without causing undue harm to any animals and confirms prior findings of a precipitous drop in blood pressure and flow following the incision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".