P.199 Variability descriptors of cerebral blood flow velocity as predictors of vasospasm in Subarachnoid Hemorrhage: A feasibility study
Bibliographic record
Abstract
Background: Transcranial Doppler (TCD) measurements poorly predict vasospasm in patients with aneurysmal subarachnoid hemorrhage (aSAH). Variability descriptors of mean cerebral blood flow velocity (mean-CBFV) may improve this prediction. We assessed the feasibility of generating reliable mean-CBFV variability metrics using extended TCD recordings in aSAH patients and healthy controls. We also explored whether these parameters are capable to discriminate aSAH patients from healthy controls, and between patients with and without vasospasm. Methods: Bilateral mean-CBFV, systemic blood pressure and heart rate were recorded for 40 minutes in 3 groups: aSAH patients (n=8) within the first 5 days post-ictus, age-matched healthy controls (n=8) and young healthy controls (n=8). We obtained linear [standard deviations, coefficient of variations, very-low, low and high-frequency power-spectra] and non-linear [Fractality, deterministic Chaos analyses] variability metrics. Results: All TCD recordings provided consistent variability metrics. aSAH patients showed higher correlation dimensions, increased high-frequency spectral power, and decreased very-low frequency power than healthy controls. aSAH patients who developed vasospasm (n=3) showed higher mean-CBFV and lower coefficient of variations than those without vasospasm (n=5). Conclusions: Descriptors of mean-CBFV variability may distinguish between aSAH patients with and without vasospasm. Future studies are required to evaluate the role of these variability parameters for risk stratification in aSAH.
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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 | 0.003 |
| 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 | 0.001 |
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".