Risk Factors for the Development of Eating Disorders in Adolescents with Early-Onset Inflammatory Bowel Diseases
Bibliographic record
Abstract
Individuals with inflammatory bowel diseases (IBDs) have an increased risk of developing psychiatric comorbidities, including eating disorders (EDs). We aimed to investigate the potential association between key disease characteristics, including psychological features, and the development of EDs in a clinical sample of adolescents with IBDs. We enrolled 52 adolescents with IBDs, 83% of whom were in clinical remission, and systematically collected additional information on disease duration, the total number of relapses, the use of steroids, and the number of hospital admissions. All participants completed a validated psychometric battery assessing psychological symptoms (Symptom Checklist-90–Revised, SCL-90-R), alexithymia (Toronto Alexithymia Scale-20, TAS-20), and ED symptomatology (Eating Disorders Inventory-3rd edition, EDI-3). About one in ten patients (9.6%) reported Eating Disorder Risk scores higher than the cut-off on the EDI-3 subscale, specifically addressing the risk of developing EDs. According to the EDI-3 scores, the risk of developing EDs directly correlated with the number of total relapses of IBDs (p < 0.05). The TAS-total scores also correlated with the number of total relapses (p < 0.01), as well as with the number of steroid cycles (p < 0.05), the number of hospital admissions (p < 0.05), and overall disease duration (p < 0.05). Our findings suggest that disease relapses increase the risk of developing both EDs and alexithymia in adolescents with IBDs. The recurrence of disease relapses should be identified and screened early on to prevent the onset of psychiatric disorders, including EDs. Research should be conducted on larger samples with different IBD phenotypes to further investigate the characteristics of patients with IBDs at risk of developing EDs.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".