Attention Deficit Hyperactivity Disorder and Dysregulated Eating
Bibliographic record
Abstract
This study was conducted to identify features of dysregulated eating in the context of children living with Attention Deficit Hyperactivity Disorder (ADHD).A deeper understanding of the association between various eating behaviors and specific moderators unique to ADHD was sought to help healthcare providers identify measurable and predictive features that direct nutritional assessment, support, and interventions.Using the Fineout-Overholt, Melnyk, Stillwell, and Williamson (2010) method, seven studies that included children aged 4 and 15 from the Czech Republic, Greece, Germany, Iran, and Korea were reviewed.Results revealed dysregulated eating patterns involving non-traditional eating schedules, increased eating frequency, episodes of overeating, preference for calorically dense food, high intake of sugary foods and beverages, and diets of higher amounts of processed and fast foods.In conclusion, underlying symptomology characteristics such as impulsivity, inattention, and emotional dysregulation may serve to mediate dysregulated eating behaviors in children living with ADHD.Early recognition and intervention could alter the progression of dysregulated eating behavior and its negative sequelae.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".