Bibliographic record
Abstract
Background: Mothers of children with intractable epilepsy are generally stressed and experienced more emotional problems. Fatigue may affect their productivity, social interactions, and their ability to take care of their children adequately. The objectives were to examine the relationship between intractable childhood epilepsy and maternal fatigue, and explore possible contributing factors. Methods: 64 consecutive mothers of children with intractable epilepsy were identified prospectively. Exclusion criteria included degenerative/metabolic disorders or life threatening illness, such as brain tumors. Fatigue was measured using a standardized 11-item questionnaire, which has been revalidated in Arabic speaking population. Results: Mothers ages were 24-45 years (mean 34) and ages of their epileptic children were 1-15 years (mean 6.7). Most children (64%) had epilepsy for >2 years, were on >1 AED (72%), and had daily seizures (47%). Thirty-four (54%) of the children had motor deficits and 83% had mental retardation (severe in 41%). Twenty-eight (44%) mothers were fatigued. Factors associated with increased maternal fatigue included child’s age <2 years (p=0.01), cryptogenic epilepsy (p=0.03), and severe motor deficits (p=0.04). Factors associated with lowered fatigue included performing regular exercise (p=0.006), lack of mental retardation (p=0.01), seizure control (p=0.05), using one AED (p=0.002), infrequent ER visits (p=0.005), and lack of recent hospitalization (p=0.005). Conclusions: Mothers of children with intractable epilepsy are increasingly fatigued. Several correlating factors were identified, mostly related to seizure control, mental and physical handicap. Strategies to manage the problem include proper education, seizure control, participation in regular exercise, social support, and psychological counseling.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.042 |
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".