Seizure-Induced Periprosthetic Femoral Fracture After Total Hip Arthroplasty in a Patient With Epilepsy: A Case Report
Bibliographic record
Abstract
Fractures secondary to seizure activity are a well-known complication in patients with epilepsy; however, periprosthetic fractures following total hip arthroplasty (THA) due to seizures are rarely documented. Nonadherence to anti-epileptic medications (AEMs) significantly increases the risk of seizure recurrence, particularly in individuals with a history of seizure-induced skeletal trauma. We present the case of a 27-year-old male with a documented history of epilepsy who sustained a right periprosthetic femoral fracture following a generalized tonic-clonic seizure. One year prior, the patient had undergone THA due to post-traumatic acetabular arthritis, which developed after a seizure-induced acetabular fracture. He later became noncompliant with his prescribed levetiracetam regimen, discontinuing use approximately one month after his initial THA. Imaging revealed a Vancouver B2 periprosthetic fracture with femoral stem subsidence. The patient underwent revision THA with open reduction and internal fixation and had an uneventful postoperative course. This case highlights the critical importance of medication adherence in patients with epilepsy, especially those with prior seizure-related orthopedic injuries. It also raises the need for further research into the long-term effects of levetiracetam on bone health. Regardless of the specific AEM, physicians should maintain a high index of suspicion for bone health deterioration and consider routine bone mineral density screening in this unique patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".