Fatigue in Inactive Auto-Inflammatory Diseases and Opportunities for Optimizing Clinical Care: A Single-Center Observational Study
Bibliographic record
Abstract
Objective: To characterize debilitating fatigue in children and adults across inactive auto-inflammatory diseases (AID), identifying distinct disease-specific fatigue phenotypes and modifiable risk factors is necessary for optimal care. Methods: A single-center cohort of consecutive patients with inactive AID between 2007 and 2024 was performed. Demographics, clinical and laboratory features, and treatment were captured. Fatigue was characterized and quantified using the PedsQL-MFS and VAS; the CES-D/CESD-R was applied to assess depression risk. Comparisons were made using non-parametric methods, multivariable regression identified risk factors of fatigue in inactive disease. Results: 66 patients were included: 39 (59%) were children; the median age at symptom onset was 4 years, at treatment start was 8 years, and study follow-up was 7 years. All patients had inactive disease at the last visit. Patients with cryopyrin-associated periodic syndromes (CAPS) had the highest Cognitive Fatigue scores (p = 0.04). Univariate analyses identified higher fatigue scores (1) in adults across all domains except Sleep/Rest (all p ≤ 0.002), (2) in patients with pathogenic/likely pathogenic variants, and (3) for disease duration ≥10 years except Sleep/Rest (all p ≤ 0.01). Depression was the single most important factor associated with fatigue in all domains (p < 0.001). In multivariable analysis, depression remained the strongest predictor of fatigue even when accounting for age, gene variant, disease duration, and treatment delay. Conclusions: Fatigue remains the major burden in AID despite the availability of effective anti-inflammatory therapies. Depression was identified as the strongest determinant of debilitating fatigue in inactive AID. Systematic screening and integrated approaches addressing both psychological and inflammatory domains are essential for optimal care.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".