Impact of Fatigue on Work Productivity, Activity Impairment, and Healthcare Resource Utilization in Inflammatory Bowel Disease
Bibliographic record
Abstract
Objectives: Fatigue is commonly reported in patients with Crohn's disease (CD) and ulcerative colitis (UC), including patients with inactive disease. We explored the impact of fatigue on healthcare utilization (HCU) and work productivity and activity impairment (WPAI). Methods: Data collected between 2017 and 2022 were analyzed from the CorEvitas IBD Registry. We compared HCU and WPAI among subjects with high fatigue (PROMIS ≥55) versus low fatigue at enrollment and subjects whose fatigue score worsened or persisted versus low fatigue at 6 months. HCU was defined as an inflammatory bowel disease-related hospitalization or emergency room visit. WPAI included presenteeism, absenteeism, and lost WPAI. Logistic regression analysis was performed. Results: Study patients (640 CD, 569 UC) reported high rates of fatigue, 47% in CD and 38% in UC, that persisted at least 6 months in 88%-89% of patients. Patients with UC with high fatigue had 3-fold higher rates of HCU and 2-3-fold more absenteeism and activity impairment than patients with low fatigue. Patients with CD with high fatigue had no difference in HCU but did experience 2-4-fold more absenteeism, presenteeism, work productivity loss, and activity impairment. On subgroup analysis of patients in remission, those with high fatigue did not have higher rates of HCU but continued to have higher rates of WPAI. Conclusions: Fatigue is associated with an increase in HCU only in the setting of concomitantly active disease. On the other hand, fatigue is associated with a negative impact on WPAI in the setting of both active and inactive disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".