P.008 Could Transient neurological Symptoms with subdural hematoma be explained by Cortical spreading depolarization Activity in Neurons? (CT-SCAN)
Bibliographic record
Abstract
Background: Transient neurological symptoms in patients with subdural hematoma (SDH) are often attributed to secondary epilepsy despite a negative workup. We believe a significant proportion of these patients could rather suffer from cortical spreading depolarization (CSD). Methods: We performed a retrospective case-control study of patients with transient neurological symptoms post-SDH evacuation between 1996 and 2017. The clinical features of patients with negative EEG were compared to those with positive EEG (ictal or interictal abnormalities) and a clinical scoring system was created. Results: 59 patients were included, 20 (34%) with a positive EEG. Speech-related symptoms (OR 4.8, p=0.018) and prolonged episodes (OR 23.1, p=0.001) were associated with a negative EEG. Clonic movements (OR 0.014, p<0.0005), impaired awareness (OR 0.013, p<0.0005), positive symptoms (OR 0.05, p<0.0001), complete response to standard antiepileptic drugs (OR 0.06, p=0.007) and mortality (OR 0.021, p=0.003) were associated with a positive EEG. We built a clinical score based on these features, which showed a 90% sensitivity and 100% specificity. Conclusions: We believe that the differences observed between both groups were driven by the presence of CSD rather than seizure in the case group. Our proposed scoring system can help predict EEG results and may be useful to identify CSD in future trials.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".