A Critical Look at Phenytoin Use for Early Post-Traumatic Seizure Prophylaxis
Bibliographic record
Abstract
BACKGROUND: The American Academy of Neurology recommended using phenytoin or carbamazepine to prevent early post-traumatic seizures (PTS) in severe traumatic brain injuries (TBI). In this study, we examined the effects of using phenytoin prophylaxis on mild, moderate, and severe TBIs. There have been no studies looking at compliance rate and side effects of systematic use of phenytoin at a large population scale. The goal of this study is to determine 1) the proportion of TBI patients receiving phenytoin prophylaxis; 2) which parameters decided when to decide administer phenytoin; 3) prophylaxis efficacy and complication rate. METHODS: We retrospectively studied all patients admitted with a TBI over a two year-period and collected the following information: age, GCS score, CT-scan Marshall grade, incidence of early PTS, incidence of phenytoin use and time delay, side effects, and incidence of over-dosage or under-dosage. RESULTS: 1008 patients were included. 5.4 % had early PTS, 2.3 % while on prophylaxis and 3.1% while not on prophylaxis, 1.9% before reaching the hospital and 1.2% prior to phenytoin administration while in hospital. Delay of administration was 5 hours. 64.8% received prophylaxis and physicians used positive CT scan as the primary decision-making parameter (p<.001). Compliance with guidelines was 99.7%. Adverse reactions occurred in 0.5%. Levels were drawn in 42.2% (52% therapeutic, 41% low, 7% high). CONCLUSIONS: Phenytoin is used according to guidelines, with CT scan being the main decision factor for its use. The frequency of early PTS rate is low and side effects are rare. However, earlier administration of phenytoin and adequate levels could further prevent early PTS.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".