Factors for cognitive impairment in adult epileptic patients
Bibliographic record
Abstract
OBJECTIVE: To analyze factors for cognitive impairment in epileptic patients. METHODS: A total of 257 epileptic patients completed clinical memory scale (CMS) and 70 of them were further surveyed with mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), digital symbol test (DSy), verbal fluency test, digit span test (DSp), Hamilton anxiety scale (HAMA) and Hamilton depression scale (HAMD). Monadic linear related analysis and multiple stepwise regression analysis were performed to evaluate the potential factors for cognitive impairment. RESULTS: Educational level was correlated with scores of cognitive tests (p < .01), with a difference between the junior high school group and senior high school group (p < .01 or p < .05). Seizure frequency was negatively correlated with CMS scores (p < .01), with a difference between the group with a seizure frequency of less than once a year and other groups (p < .01). The kind of antiepileptic drugs (AEDs) was negatively correlated with CMS scores (p < .01), with a difference between the single-drug group and the group taking more than two kinds of AEDs (p < .01). Depression scores were negatively correlated with MMSE, MoCA, DSy, DSp (p < .01 or p < .05), disease duration negatively with DSy (p < .01), and age negatively with MoCA (p < .05). Seizure type was correlated with DSy, and general seizure fared worse in the tests than other seizure types (p < .05). CONCLUSION: Educational level, seizure frequency, kinds of AEDs and depression can affect the cognitive function of epileptic patients. High educational level, good seizure control, single-drug treatment and healthy psychological state are protective factors for cognitive function of epileptic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".