P.089 A literature search using titles and key terms was conducted for articles containing brain death ancillary testing diagnosis, and CTP as primary focus
Bibliographic record
Abstract
Background: Ancillary testing assist in the diagnosis of brain death. While traditionally, lack of blood flow (BF) in the intracranial arteries constitutes conclusive evidence that the brain is dead, there is apparent discrepancy between the BF, and sufficient cerebral perfusion; In 15% of patients with confirmed clinical diagnosis of brain death, BF is still preserved. In these patients, cerebral perfusion is significantly impaired suggesting that cerebral perfusion rather than BF more accurately assesses brain function. We aim to present a history of brain death, its pathophysiology, and ancillary tests utilized for its diagnosis- specifically CT Perfusion studies. Methods: A literature search using titles and key terms was conducted for articles containing brain death ancillary testing diagnosis, and CTP as primary focus. Results: Across selected studies, CTP diagnosed brain death with 100% positive predictive value, as none of the patients were proven not-dead on follow-up. The early prediction of mortality outcome in these patients with proven high mortality rate may help decisions for withdrawal of life support. It may also facilitate procurement of organs for transplants. Conclusions: Although clinical assessment is the gold standard method of brain death determination, CTP has shown promising results that could alter our current clinical approach.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.041 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".