Paroxysmal Slow Wave Events as a diagnostic and predictive biomarker for post-traumatic epilepsy
Bibliographic record
Abstract
Abstract Traumatic brain injury (TBI) is a major global health concern, affecting more than 40 million people annually. While most cases are mild and present with light symptoms, repeated mild injuries can result in delayed brain pathologies, including cognitive decline, neuropsychiatric complications, and post-traumatic epilepsy (PTE). PTE refers to recurring, unprovoked seizures occurring at least one week after TBI. While the link between moderate to severe TBI and PTE is well established, the epileptogenesis after repetitive mild TBI (rmTBI) is seldom studied. Currently, there are no biomarkers to identify those at risk of developing PTE, and its diagnosis is challenging. Here, we used a rat model to study PTE following rmTBI and assessed human EEG data to identify potential biomarkers for PTE. We employed a closed head TBI model to induce rmTBI, and recorded brain activity using electrocorticography (ECoG) between 2- and 6-months post-injury. Behavioral assessments and post-mortem analysis were also conducted. In humans, we analyzed EEG recordings from the Temple University database to investigate the potential of EEG-derived features for diagnosing PTE. At 6 months post injury, 70% of rmTBI animals developed PTE, compared to 22% in the control group (P=0.01). While neurological assessments following injury did not predict PTE, paroxysmal slow wave events (PSWEs) were found to be a reliable biomarker for PTE prediction. In humans, the percentage time in PSWEs was significantly elevated in PTE patients with epileptiform activity. In conclusion, we suggest PSWEs as a non-invasive, cost-effective biomarker for PTE in rodents and human patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".