Study about cognitive performances in elderly patients with epilepsy
Bibliographic record
Abstract
A significant proportion of patients with epilepsy are at an increased risk of cognitive impairment. The goal of our study was to assess the cognitive state in elderly patients with idiopathic epilepsy. We studied 48 patients (20 men and 28 women, mean age 71,1± 4,5 years and mean educational level 10,8 ±1 years). They had a duration of the disease at least 6 years and a stable medication for 3 months prior the study. The types of seizures were as follows: 9 of the patients had tonic-clonic seizures, 5 patients had tonic seizures, 3 patients with clonic seizures, 18 patients with simple partial seizures and 13 patients with complex partial seizures. We have also included in our study a control group composed of 52 control subjects with the same range of educational level and age. The controls had no neurological or psychiatric illness, no family history of seizures, no history of medication. To assess the cognitive state we tested the patients using Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at baseline, after 6 months and one year later. Our study in dynamics notice that the patients group showed a cognitive impairment after 6 months of study, but not considerable statistically in comparison to the control group. One year later, the patients with epilepsy had significant statistically cognitive decline in comparison to the control group (p < 0,05). The most cognitive affected were patients with tonic-clonic seizures. The patients on antiepileptic polytherapy showed greater cognitive impairment than those patients on monotherapy. The most cognitive domains affected were attention, concentration and memory.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".