Obesity and its association with generalised epilepsy, idiopathic syndrome, and family history of epilepsy
Bibliographic record
Abstract
Aim. Previous studies support the concept that obesity is a common comorbid condition in patients with epilepsy (PWE). In this study, we present the body mass index (BMI) and data from a survey to assess physical activity in a sample of PWE from an epilepsy clinic. Methods. Between June of 2011 and January of 2013, 100 PWE from an adult epilepsy clinic were included. We obtained BMI, waist circumference, and information regarding physical activity using a standardised questionnaire. Clinical, demographic, electrographic, and imaging parameters were collected from charts. Results. Mean age of patients was 40 ± 14 (18-77) years. The BMI distribution was as follows: 2 patients (2%) underweight, 26 (26%) normal weight, 34 (34%) overweight, 25 (25%) obese, and 13 (13%) with morbid obesity. In our study, obesity was defined as having a BMI ≥ 30. We found 38 (38%) patients in this range. There was no difference in the rate of drug-resistant epilepsy between obese and non-obese patients (55 vs. 55%; p=0.05). Leisure time habit was reported in 82% of obese patients and 79% of patients without obesity. Overall, the most frequent activity was walking (70%). Factors associated with obesity were generalised epilepsy (OR: 2.7, 1.1-6.6; p=0.012), idiopathic syndrome (OR: 2.7, 1.04-7; p=0.018), and family history of epilepsy (OR: 6.1, 1.5-24.2; p=0.002). Conclusion. Our study suggests an association between obesity, idiopathic generalised epilepsy, and family history of epilepsy. Our study shows that PWE are physically active and there is no clear relation between exercise and obesity. We could not identify any association between drug-resistant epilepsy and obesity. Absence of direct comparison with a control non-epileptic population; a cross-sectional design not allowing evaluation of a causal association among variables; and reliance on self-reported physical activity are to be considered as limitations of the present study.
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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) | 0.003 | 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".