Risk of Epilepsy Following Traumatic Brain Injury: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Limited evidence has explored the impact of traumatic brain injury (TBI) on posttraumatic epilepsy with control cohort for comparison. In addition, we could not find any review to identify the effect of TBI on the outcomes. Thus, we conducted this study to compare the risk of epilepsy between individuals with TBI and without TBI. METHODS: Systematic and comprehensive search was carried out in the following databases and search engines: EMBASE, Cochrane, MEDLINE, ScienceDirect, and Google Scholar from 1954 until January 2022. The Newcastle Ottawa (NO) Scale was utilized to assess the risk of bias. Meta-analysis was carried out using the random-effects model, and pooled odds ratio (OR) along with 95% CI was reported. RESULTS: In total, we included 10 studies satisfying inclusion criteria. Most studies had good to satisfactory quality. The pooled OR was 4.25 (95% CI, 1.77-10.25; I2 = 100%), indicating that the individuals with TBI had 4.25 times higher risk of having epilepsy than individuals without TBI, and this association was statistically significant ( P = .001). Subgroup analysis based on the years of follow-up revealed that the patients within 5 years post-TBI had the highest risk of epilepsy (pooled OR = 7.27; 95% CI, 3.61-14.64). CONCLUSION: Individuals with TBI had a significantly higher risk of epilepsy than the individuals without TBI, irrespective of the duration of the injury. Hence, long-term follow-up of the individuals with TBI is necessary to prevent any adverse consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".