The Possibilities of Assessing the Quality of Life in Inflammatory Bowel Diseases
Bibliographic record
Abstract
Inflammatory bowel diseases (IBD) in children are chronic immune-mediated conditions that significantly affect the quality of life (QOL). QOL assessment tools designed specifically for children allow us to quantify the degree of the disease’s impact on various aspects of their lives, such as daily activity, emotional state, social interactions, etc. The research results demonstrate that IBD has a negative impact on children’s QOL, leading to lower school performance, limited social activity, and psychological problems such as anxiety and depression. The severity of the disease, the frequency of exacerbations, the presence of symptoms, extra-intestinal manifestations, the need to follow a diet, frequent hospitalization, and side effects of drug therapy are factors associated with lower QOL. Improving QOL is one of the key goals of treatment, along with achieving clinical and endoscopic remission. Interventions aimed at improving QOL in children with IBD include pharmacological therapy, nutritional support, and psychosocial care. Regular QOL assessment allows doctors and parents to better understand the child’s needs, identify problem areas, and develop customized strategies aimed not only at controlling the disease, but also at improving overall well-being. The article summarizes data on existing methods for assessing QOL in pediatrics, including in the pathology of the gastrointestinal tract and, in particular, in IBD.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".