Abstract 55: Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage Using Variations of Dynamic Cerebral Autoregulation
Bibliographic record
Abstract
The risk of delayed cerebral ischemia (DCI) after cerebral aneurysm rupture is increased in patients with large cerebral artery vasospasm (VS). The predictive value of VS is, however, far to be optimal. Assessment of cerebral microcirculation impairment using dynamic cerebral autoregulation (DCA) measurement might improve our ability to predict DCI in these patients. Methods. We included consecutive patients with low-grade aneurysmal subarachnoid hemorrhage (SAH) (WFNS 1 to 3) within 4 days of aneurysm rupture. DCA was evaluated using the moving correlation coefficient Mx calculated from spontaneous fluctuations of cerebral blood flow velocities and arterial blood pressure. Impairment of DCA was defined as Mx ≥ 0.3. Transcranial colour-coded sonography was performed every 48 hours to look for VS. Diagnosis of VS was based on a Lindegaard ratio >3 (MCA), or mean flow velocities > 130 cm/s (ACA). DCI was defined as neurologic deterioration caused by brain ischemia confirmed by brain imaging. Results. Thirty patients (19 women; mean age ± SD: 44.7 ± 12.1 years) were included. Twenty (66.7%) patients had VS. DCI occurred in six (20%) patients after a median delay of 10 days (range 8 to 13 days). Mx (mean ± SD) was heightened at baseline (0.43 ± 0.2) and at day 7 (0.46 ± 0.2), then decreased at day 14 (0.37 ± 0.18 (p = 0.034). Isolated measures of VS and Mx were not associated with DCI. However, the elevation of Mx between baseline and day 7 in patients with VS was predictive of subsequent DCI (positive likelihood ratio=5.01; negative likelihood ratio=0.19; P=0.005). Conclusion. Findings suggest that DCA assessment may help to predict better the risk of DCI after cerebral aneurysm rupture than the simple recordings of mean flow velocities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".