Post-traumatic seizure disorder following acquired brain injury
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to evaluate the effectiveness of prophylactic anticonvulsant pharmacological strategies for the prevention of seizure disorders following acquired brain injury (ABI) to provide guidance for clinical practice based on the best available evidence. METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2005 was conducted focusing on treatment interventions available for post-traumatic seizures following ABI. The evidence for the efficacy of a given intervention was ranked as strong (supported by at least two randomized controlled trials (RCTs), moderate (supported by a single RCT), or limited (supported by other types of studies in the absence of RCTs). RESULTS: Based on a previous meta-analysis and the findings of this review, there is strong evidence that prophylactic anticonvulsant therapy decreases the occurrence of early seizures but only within the first week post-injury. Moreover, the evidence indicates that prophylactic anticonvulsant therapy does not decrease the incidence of seizure onset more than one week post-injury. In children, there is moderate evidence that prophylactic phenytoin does not reduce the incidence of early or late seizures. The efficacy of anticonvulsants after the development of seizures has not been specifically studied in ABI. CONCLUSIONS: Prophylactic anti-convulsants are effective in reducing seizures in the first week post-injury in adults. However, they do not reduce the occurrence of seizures after the first week.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.003 | 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".