Bibliographic record
Abstract
Introduction: Focal epilepsy accounts for 60% of all clinical epilepsy cases, while temporal lobe epilepsy (TLE) accounts for 40%.Cognitive impairment such as language, attention, executive function, and memory impairment are common in temporal lobe epilepsy, and researchers believe that seizure frequency and duration can cause severe hippocampal sclerosis, as well as a secondary impact on neuronal metabolism and structure, which leads to cognitive impairment.Aim: The goal is to determine the cognitive impairment of temporal lobe epilepsy, the clinical characteristics and risk factors. Materials and Methods:A cross-sectional study of 50 adults with temporal lobe epilepsy from the National Center for Mental Health used a questionnaire and Montreal Cognitive Assessment (MoCA).Results: We included 50 adults aged 26 to 61 with temporal lobe epilepsy, 52% of respondents were male, 48% were female, with an average age of 43.78±8.20 years.The study found that 92% of individuals showed cognitive impairment, with mean MoCA scores of 17.50±4.57.Long lasting seizure is a high-risk factor for cognitive function, with statistical significance (p=0.03).Seizure onset age was linked with increased attention impairments and poor visuospatial function (p=0.005), while higher seizure frequency was associated with decreased calculation (p=0.04),language (p=0.009), and drawing skills (p=0.013).Conclusion: Our study found that 58% of respondents showed moderate cognitive impairment.Low education level, earlier age of seizure onset, high seizure frequency, extended seizure duration, presence of aura, and refractory temporal lobe epilepsy all have a statistically significant effect on cognitive impairment.
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 | 0.000 | 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) | 0.002 | 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".