Effective Factors in Cognitive Functions of Patients with Temporal Lobe Epilepsy
Bibliographic record
Abstract
Background: Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and increases the risk of cognitive impairment, negatively impacting the quality of life of affected individuals. Objectives: This study aimed to investigate cognitive function in patients with low socioeconomic status affected by TLE and identify factors influencing such function. Methods: This case-control study, conducted between July 2021 and August 2022, compared the cognitive function of 40 patients affected by TLE to 92 healthy controls. The Montreal cognitive assessment (MoCA) was used for neurocognitive evaluation. Data analysis was performed using SPSS 25.0 for Windows. Results: The mean age of the patient group was 33.35 years, compared to 35.37 years in the control group. Moreover, 70% of patients affected by TLE displayed cognitive impairment and demonstrated lower performance in cognitive functions than the control group (P < 0.05). Significant correlations were identified between overall MoCA scores and several factors, including seizure frequency, educational level, polytherapy, disease duration, and self-esteem scores (P < 0.05). Multivariate analysis revealed that seizure control and higher educational level were statistically significant predictors of overall MoCA scores in patients affected by TLE. Conclusions: In low-income patients affected by TLE, seizure control and a higher educational level emerged as predictors of cognitive performance. These findings underscore the importance of identifying and managing comorbidities and the need for tailored cognitive rehabilitation programs for this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".