Assessing Dietary Practices of Children with Avoidant Restrictive Food Intake Disorder (ARFID) - A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The current study aimed to find children's dietary patterns with Avoidant Restrictive Food Intake Disorder (ARFID) and its characteristics in children. METHODOLOGY: This mixed-method cross-sectional study was conducted in April 2021 among children with Avoidant Restrictive Food Intake Disorder. Thirty participants of the age group (5-12 years) were selected from the Pediatrics and Children Ward of Ittefaq Hospital, Lahore, with a purposive sampling technique. Outpatient children aged between 5 and 12 years were included, whereas the children who had any congenital disabilities and were diagnosed with any other eating disorder were excluded. A focus group discussion was held for the formulation of the questionnaire. A diagnostic criteria questionnaire for ARFID was taken from the Canadian Pediatric Surveillance Program 2017, including anthropometric data and general characteristics/behavioral features. A self-structured food frequency questionnaire was used. Informed consent was taken from all participants. Qualitative interviews were reported as thematic analysis, and Quantitative data were analyzed using SPSS version 25 as statistical analysis for data visualization, simple statistics to generate summary charts, and customized graphs. RESULTS: Patients with ARFID were primarily female and relatively young, with a mean age of 8.35±0.46. Most of the participants were underweight (77%). The mean BMI (kg/m2) of the children was 13.7±0.26. Most patients with ARFID also reported a lack of interest in eating, loss of appetite, aversion due to sensory characteristics and avoidance of certain foods. CONCLUSION: ARFID is prevalent in all populations, but the lack of awareness among healthcare professionals and the general population makes the diagnosis difficult. KEYWORDS: ARFID, Eating Disorders, Food aversions, Food Frequency Questionnaire, Loss of appetite, Underweight
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".