Seizure Prophylaxis Following Moderate to Severe Traumatic Brain Injury: Retrospective Investigation of Clinical Practice and the Impact of Clinical Guidelines
Bibliographic record
Abstract
Background Post-traumatic seizure (PTS) is a major complication of traumatic brain injury (TBI). However, there has been controversy in the literature regarding whether anticonvulsants should be used prophylactically to prevent it, and there is significant variability in practice. The objective of this study is to describe seizure prophylaxis practices after moderate to severe TBI and to determine whether the use of seizure prophylaxis increased following the recommendations of the Quebec Institut National d'Excellence en Santé et Services Sociaux and the Ontario Neurotrauma Foundation (INESSS-ONF) guidelines. This study will also compare the characteristics of patients who receive the recommended prophylaxis and those who do not. Methods All adult patients admitted to a level-1 trauma centre for moderate to severe TBI were eligible for this study (n = 96). Medical records including patient age, sex, Glasgow Coma Scale (GCS) score, mechanism of injury, and occurrence of PTS were reviewed in a retrospective manner regarding the administration of recommended seizure prophylaxis. Results The proportion of patients receiving the recommended seizure prophylaxis was 8%. There was no significant increase after the release of the INESSS-ONF guidelines (p: 0.38). There were no significant differences in demographics, injury characteristics, or rates of early PTS between patients receiving the recommended prophylaxis and those not receiving it (p: >0.05). Conclusion The results indicate that the use of the recommended seizure prophylaxis after moderate to severe TBI is low and that the release of the INESSS-ONF guidelines did not increase its use. Patient and injury factors do not appear to influence the use of seizure prophylaxis. These results highlight variability in seizure prophylaxis practices and the importance of understanding local practice patterns. Implementation strategies should be identified to increase adherence to the recommendations and improve patient care.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".