Pain Trajectories in Pediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVES: This study aimed to characterize pain intensity (average, worst) and disease severity in youth with inflammatory bowel disease in the 12-month postdiagnosis, and to examine the relation between pain and risk (disease severity) and resilience (optimism, pain self-efficacy) factors over time. METHODS: Data collection ran from February 2019 to March 2022. Newly diagnosed youth aged 8 to 17 with IBD completed numerical rating scales for average and worst pain intensity, Youth Life Orientation Test for optimism, and Pain Self-Efficacy Scale for pain self-efficacy through REDCap; weighted Pediatric Crohn's Disease Activity Index and the Pediatric Ulcerative Colitis Activity Index were used as indicators of disease severity. Descriptive statistics characterized pain and disease severity. Multilevel modeling explored relations between variables over time, including moderation effects of optimism and pain self-efficacy. RESULTS: At baseline, 83 youth ( Mage =13.9, SD=2.6; 60.2% Crohn's disease; 39.8% female) were included. Attrition rates at 4 and 12 months were 6.0% and 9.6%, respectively. Across time, at least 52% of participants reported pain. Participants in disease remission increased from 4% to 70% over 12 months. Higher disease severity predicted higher worst pain, regardless of the time since diagnosis. Higher pain self-efficacy (1) predicted lower average and worst pain, especially at later time points and (2) attenuated the association between disease severity and worst pain when included as a moderator. Higher optimism predicted lower worst pain. DISCUSSION: Pain is prevalent in pediatric inflammatory bowel disease and impacted by disease severity, pain self-efficacy, and optimism. Findings highlight modifiable intervention targets.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".