Efficacy of antiseizure prophylaxis in chronic subdural hematoma: a cohort study on routinely collected health data
Bibliographic record
Abstract
OBJECTIVE: Chronic subdural hematoma (CSDH) is a common neurosurgical condition that can result in significant morbidity. The incidence of epileptic events associated with CSDH reported in the literature varies considerably and could potentially increase morbidity and mortality rates. The effectiveness of antiepileptic prophylaxis for this indication remains unclear. The primary objective of this study was to assess the relevance of anticonvulsant prophylaxis in reducing seizure events in patients with CSDH. METHODS: All consecutive cases of CSDH from January 1, 2005, to May 30, 2014, at the Hôpital de l'Enfant-Jésus in Quebec City were retrospectively reviewed. Sociodemographic data, antiepileptic prophylaxis use, incidence of ictal events, and clinical and radiological outcome data were collected. Univariate analyses were done to measure the effect of antiepileptic prophylaxis on ictal events and to identify potential confounding factors. Multivariate logistic regression was performed to evaluate factors associated with epileptic events. RESULTS: Antiepileptic prophylaxis was administered in 28% of the patients, and seizures occurred in 11%. Univariate analyses showed an increase in the incidence of ictal events in patients receiving prophylaxis (OR 5.92). Four factors were identified as being associated with seizures: septations inside the hematoma, membranectomy, antiepileptic prophylaxis, and a new deficit postoperatively. Antiepileptic prophylaxis was not associated with seizures in multivariate analyses. CONCLUSIONS: Antiepileptic prophylaxis does not seem to be effective in preventing seizures in patients with CSDH. However, due to the design of this study, it is difficult to conclude definitively about the usefulness of this prophylactic therapy that is widely prescribed for this condition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".