Exploring multimodal biomarker candidates of post‐traumatic epilepsy following moderate to severe traumatic brain injury: A systematic review and meta‐analysis
Bibliographic record
Abstract
This review systematically analyzes potential biomarker candidates for post-traumatic epilepsy (PTE) in humans who have experienced moderate to severe traumatic brain injury (TBI). Focusing on biomarkers across biofluid-based protein, genetic, neuroimaging, and neurophysiological categories, this review distinguishes between TBI patients who develop PTE and those who do not. The review adheres to established methodologies outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Data presentation follows the Meta-analyses of Observational Studies (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Medline, Embase, and Web of Science were systematically searched and yielded 7538 records, of which 18 met inclusion criteria (moderate-severe TBI in humans, follow-up of at least 6 months, and no prior history of epilepsy). The review aggregates data from 15 cohort and 3 case-control studies (risk of bias was assessed using the Newcastle-Ottawa Scale). Statistically significant biomarkers were identified, with neurophysiological biomarkers showing the strongest effect size in a two-study meta-analysis. PTE, a severe long-term outcome of TBI affecting 2% to 53% of individuals with TBI, lacks validated biomarkers for forecasting development, crucial for designing preventive clinical trials. A multimodal approach, integrating biofluid-based protein, genetic, neuroimaging, and neurophysiological data, offers a promising strategy to enhance the predictability of PTE development and, potentially, its treatment. The study's protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO (Registration ID: CRD42023470245).
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".