Oxidative Stress Measurement and Prediction of Epileptic Seizure in Children and Adults With Severe Motor and Intellectual Disabilities
Bibliographic record
Abstract
BACKGROUND: The medical care of severe motor and intellectual disabilities (SMID) depends on the empirical medical care. Epileptic seizure specific to SMID is difficult to suppress using anti-epileptic drugs, and its tendency to persist for long periods poses an issue. The present study was undertaken to evaluate the relationship between epileptic seizure in cases with SMID and oxidative stress in the living body by examining endogenous antioxidants, the degree of oxidation (reactive oxygen metabolites (d-ROMs)), and the biological antioxidant potential (BAP) as indicators. METHODS: Target patients were 43 SMID epilepsy patients. Blood was sampled before breakfast and medication. As for the specimen, d-ROMs and BAP were measured using the free radical analyzer. RESULTS: The present study did not reveal any correlation between endogenous antioxidants (albumin) and the frequency of epileptic seizures. On the other hand, d-ROMs were correlated with the frequency of epileptic seizure. In particular, strong correlations between the frequency of epileptic seizures and the d-ROMs/BAP ratio as well as the BAP/d-ROMs ratio were noted. CONCLUSIONS: These results indicate that the use of d-ROMs and BAP as biomarkers can provide a tool for predicting the prognosis of epileptic seizures in patients with SMID.
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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 | 0.000 |
| 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".