P.088 Computed tomography angiography for diagnosis of brain death; a technical review
Bibliographic record
Abstract
Background: Brain death is defined as an irreversible cessation of all brain activity. Ancillary tests are an objective way to support an initial diagnosis of brain death. Computed tomography angiography (CTA) is an imaging modality utilized as an ancillary mean to assist clinicians with such diagnosis. Different criteria and scoring systems have been proposed, however clear criteria are yet to be recognized to demonstrate full brain circulatory arrest. We aim to discuss different scoring systems presented in the literature and make evidence-based recommendations. Methods: A literature search using titles and key terms was conducted for articles containing brain death ancillary testing diagnosis, and CTA as primary focus. Results: CTA has the benefits of being non-invasive, fast, readily and widely available and it is especially useful in unstable patients. It is essential, however, to confirm intravascular injection of contrast injection by checking opacification of External Carotid Artery branches on CTA to prevent false diagnoses. Conclusions: When faced with the challenging decision to declare brain death in a patient, radiologists often face great apprehension and concern for the large responsibility bestowed upon them. It is critical for radiologist to understand that the final diagnosis of brain death is based on clinical criteria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".