Suboptimal seizure control is associated with increased risk of MCI among Adult Patients with Epilepsy
Bibliographic record
Abstract
BACKGROUND: Epilepsy is associated with increased risk for dementia, which adversely impacts the quality of life for patients and their families. Mild cognitive impairment (MCI) is the prodromal stage of dementia offering an important window for intervention. However, the epilepsy related risk factors for MCI are not well understood. The ongoing Mild Cognitive Impairment among Adult Patients with Epilepsy (MCAPE) study is a longitudinal study evaluating the prevalence and risk factors for MCI amongst adult patients with epilepsy (PWE). This abstract summarizes the interim findings. METHOD: Adult PWE were recruited from outpatient clinics at a tertiary centre, the National Neuroscience Institute, Singapore. Patients with previous stroke, known dementia, or intellectual disability were excluded. Participants underwent a self-administered questionnaire on their lifestyle risk factors and epilepsy-specific factors, followed by cognitive testing with the Mini Mental State Assessment (MMSE) and Montreal Cognitive Assessment (MOCA). A MOCA cut-off of <27 was used to diagnose MCI. Logistic regressions evaluated the associations of clinical risk factors with presence of MCI, corrected for age and education as confounders. RESULT: Forty-six participants were recruited and completed cognitive testing. Twenty-three participants (50.0%) had subjective cognitive complaints. Cognitive assessment revealed that 16 participants (34.8%) had MCI. Among epilepsy factors, multivariate logistic regression found that a seizure frequency of once a month or more was strongly associated with increased risk of MCI (OR 109, p = 0.004). Epilepsy subtype, duration and use of multiple anti-seizure medications were not significantly associated with MCI. CONCLUSION: This pilot cross-sectional study showed that MCI is a significant problem among PWE, and that suboptimal seizure control was associated with increased risk of MCI. Prospective studies are need to determine effective interventions to mitigate MCI among PWE.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".