Computed Tomography Perfusion for the Diagnosis of Brain Death: A Technical Review
Bibliographic record
Abstract
Timely diagnosis of brain death (BD) is critical as it prevents unethical and futile continuation of support of vital organ functions when the patient has passed. Furthermore, it helps with avoiding the unnecessary use of resources and provides early opportunity for precious organ donation. The diagnosis of BD is mainly based on careful neurological assessment of patients with an established underlying diagnosis of neurological catastrophe capable of causing BD.Ancillary testing, however, is tremendously helpful in situations when the presence of confounders prevents or delays comprehensive neurological assessment. Traditionally, four-vessel digital subtraction angiography and computed tomography angiography have been used for blood flow (BF) examinations of the brain. The lack of BF in the intracranial arteries constitutes conclusive evidence that the brain is dead. However, there is an apparent discrepancy between the BF and sufficient cerebral perfusion; several studies have shown that in 15% of patients with confirmed clinical diagnosis of BD, BF is still preserved. In these patients, cerebral perfusion is significantly impaired. Hence, measurement of cerebral perfusion rather than BF will provide a more precise assessment of the brain function.In this review article, we discuss a brief history of BD, our understanding of its complex pathophysiology, current Canadian guidelines for the clinical diagnosis of BD, and the ancillary tests-specifically CT perfusion of the brain that help us with the prompt and timely diagnosis of BD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".