Dynamic Computed Tomography Angiography in Suspected Brain Death: A Noninvasive Biomarker
Bibliographic record
Abstract
PURPOSE: Neurologic determination of death or brain death is primarily a clinical diagnosis. This must respect all guarantees required by law and should be determined early to avoid unnecessary treatment and allow organ harvesting for transplantation. Ancillary testing is used in situations in which clinical assessment is impossible or confounded by other factors. Our purpose is to determine the utility of dynamic computed tomographic angiography (dCTA) as an ancillary test for diagnosis of brain death. MATERIALS AND METHODS: We retrospectively reviewed 13 consecutive patients with suspected brain death in the intensive care unit who had dCTA. Contrast appearance timings recorded from the dCTA data were compared to findings from 15 controls selected from patients who presented with symptoms of acute stroke but showed no stroke in follow-up imaging. RESULTS: The dCTA allows us to reliably assess cerebral blood flow and to record time of individual cerebral vessels opacification. It also helps us to assess the intracranial flow qualitatively against the flow in extracranial vessels as a reference. We compared the time difference between enhancement of the external and internal carotid arteries and branches. In all patients who were brain dead, internal carotid artery enhancement was delayed, which occurred after external carotid artery branches were opacified. CONCLUSION: In patients with suspected brain death, dCTA reliably demonstrated the lack of cerebral blood flow, with extracranial circulation as an internal reference. Our initial results suggest that inversion of time of contrast appearance between internal carotid artery and external carotid artery branches at the skull base could predict a lack of distal intracranial flow.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".