Eating habits and behaviors in children with Dravet syndrome: A case–control study
Bibliographic record
Abstract
This study evaluated food preferences and eating behaviors of individuals with Dravet syndrome. Patients diagnosed with Dravet syndrome were recruited, as well as a control group composed of siblings of patients with epilepsy (any form). The Food Preference Questionnaire and the Child Eating Behavior Questionnaire were completed by caregivers along with two open-ended questions regarding eating challenges. Seventy-eight participants (45 with Dravet syndrome and 33 controls) were included. Compared to controls, mean scores for food preference were lower for fruits (p = .000099), meats and fish (p = .00094), and snacks (p = .000027) in Dravet syndrome. People with Dravet syndrome also had less emotional overeating (p = .0085) and food enjoyment (p = .0012), but more slowness in eating (p = .00021) and food fussiness (p = .0064). In a subgroup analysis of only pediatric (age <18 years) patients, similar results were observed for both food preferences and eating habits. In qualitative data, caregivers most commonly reported difficulties with fixation on specific foods. This study demonstrates specific food preferences and challenging eating behaviors in individuals with Dravet syndrome. These data provide potential avenues for nutritional interventions and behavioral therapies to increase the quality of life of patients and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".