P.126 Prediction of cerebral vasospasm using radiographical and clinical features: a machine learning model
Bibliographic record
Abstract
Background: Cerebral vasospasm is a feature of delayed cerebral ischemia that can occur after subarachnoid hemorrhage from cerebral aneurysm rupture. CTa is the universal first line diagnostic modality (sensitivity 79.6%, specificity 93.1%). We aim to determine whether specific vasospasm-associated radiographical and clinical features predicts cerebral vasospasm with comparable accuracy. Methods: Our cohort included 403 patients between 2006-2019. We used clinical predictive features including: day since rupture, transcranial doppler Lindegaard ratio, MCA velocity, ICA velocity, physical examination, and radiographical predictive features including: volume of hematoma, artifact, aneurysm, as our training dataset with true positives being digital subtraction angiography confirmed vasospasm. We used a decision-tree classifier from Scikit-learn library for training and testing of the model. Results: Our model trained on clinical and radiographical predictive features achieved sensitivity 93%, specificity 67%, F1 score 0.88. When using only radiographical features, we reached sensitivity 90%, specificity 55%, F1 score 0.83. When using only clinical features, we reach sensitivity 70%, specificity 93%, F1 score 0.87. Conclusions: We show that our vasospasm predictive model achieves adequate sensitivity, specificity, and F1 scores when compared to CTa. With further increase in dataset and fine-tuning of hyper-parameters, it is possible that our model may be used to optimize the vasospasm management pipeline.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".