One-Year Analysis of Risk Factors Associated With Cognitive Impairment in Newly Diagnosed Epilepsy in Adults
Bibliographic record
Abstract
Cognitive impairment (CI) occurs in people with epilepsy, affecting their quality of life. This study aimed to identify factors associated with CI in adult patients with newly diagnosed epilepsy. Additionally, we sought to determine whether any particular cognitive function is impaired predominantly by anti-seizure medications or by other factors. We enrolled 229 patients with newly diagnosed epilepsy and 191 participants were followed up for 1 y. We used the Montreal Cognitive Assessment as a tool to quantify CI. The sub-item scores were also collected to assess whether any aspects of CI are predominantly affected by anti-seizure medication treatment. Subjective memory decline due to anti-seizure medications was also recorded. One-hundred-and-two participants (44.5%) had CI onset before anti-seizure medication treatment. Aging, low education level, stroke or brain surgery etiology, and anxious symptoms were identified as risk factors for CI before anti-seizure medications use. Brain surgery for the young, anxious status for the middle-aged, and depressive status for the elderly were risk factors for CI at different ages. The elderly PWE had worse memory than the others. PWE with TLE had worse cognition, especially in memory and naming. The overall impact of anti-seizure medications on cognition was mild. Refractory epilepsy was a predictor of cognitive decline. Subjective memory decline was predicted by high-risk treatment and by a finding of refractory epilepsy. Clarifying the risk factors for CI can help the physician to assess the probable risk of CI for each individual before the start of anti-seizure medication treatment, which may lead to better compliance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".