Postictal vertebral fractures: Incidence, risk factors, and clinical outcomes. A systematic review
Bibliographic record
Abstract
Vertebral fractures in postictal patients with no prior acute or chronic trauma history are rare but can lead to severe neurological complications if missed during the initial evaluation. What are the prevalence and risk factors associated with vertebral fractures? What are the characteristics of these fractures in terms of their location, severity, and clinical outcome? A comprehensive literature search using MeSH terms was conducted. Data from previously published studies and three new cases from the authors' institution were collected. The variables included demographic information, seizure history, medical comorbidities, fracture characteristics, and treatment details. Descriptive statistics summarize the data. Inferential statistics ( t -test, Mann-Whitney U test, chi-squared test, Fisher's exact test, and logistic regression) were used to identify the risk factors for vertebral fractures. The review included nine studies with a total of 19 patients, and three additional unpublished cases from the authors' institution. The median patient age was 50 years, with male predominance. No significant differences were found between the groups in osteoporosis, seizure history, antiepileptic drug use, or alcohol or drug abuse. The most common sites of fracture were the midthoracic spine for compression fractures and thoracolumbar junction for burst fractures. Risk factors for fractures include greater muscle mass, lower bone mineral density, longer convulsions, and recurrent convulsions. The mechanism of injury involved violent muscle contractions during seizures. This review summarizes the current knowledge and provides evidence for best practices in the management of seizure-induced vertebral fractures. • Postictal vertebral fractures are rare but severe complications of generalized tonic-clonic seizures. • The risk factors include seizure history, increased muscle mass, and decreased bone mineral density. • Thorough neurological examination is crucial in patients with a history of seizures. • Antiepileptic drugs may decrease bone mineral density by increasing vitamin D catabolism.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".