Association between serum homocysteine levels and the frequency of epilepsy-related cognitive dysfunction: a cross sectional study in patients with epilepsy in Mataram, Indonesia
Bibliographic record
Abstract
Background: In patients with epilepsy (PWE), cognitive dysfunction becomes an important comorbidity. In PWE, cognitive dysfunction is thought to be linked to the processes of neurodegeneration that are triggered by the rise in homocysteine levels. Objective: To determine whether PWE in Mataram, Indonesia, have a higher prevalence of cognitive dysfunction and higher serum homocysteine levels. Methods: 81 PWE were recruited for this cross-sectional study from Mataram's three primary referral hospitals. The patients' serum homocysteine levels were measured by ELISA using five milliliters of fasting blood. The patients' sociodemographic (age, gender, and educational level) and clinical (etiology of epilepsy and treatment duration) characteristics were also gathered. Indonesian version of The Montreal Cognitive Assessment (MoCA-Ina) was used to evaluate the cognitive status. The association between homocysteine, clinical, and socio-demographic data, as well as the cognitive dysfunction frequency in PWE was determined with final logistic regression model. Results: The patients’ age was 33 years old on average. PWE had a cognitive dysfunction prevalence of 76.5 percent. PWE's high prevalence of cognitive dysfunction was not associated with serum homocysteine levels in the final logistic regression model (p > 0.05). Patients' clinical and sociodemographic characteristics were also not associated with a high prevalence of cognitive dysfunction (p > 0.05). Conclusion: In Mataram, Indonesia, the high prevalence of cognitive dysfunction among PWE was not linked to serum homocysteine levels.
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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 | 0.001 |
| 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".