Recurring Fatigue After Biologic Administration: Patient-Reported Data from the Dutch Biologic Monitor
Bibliographic record
Abstract
BACKGROUND: Fatigue is a common problem in immune-mediated inflammatory disease (IMID) patients, significantly impacting their quality of life. OBJECTIVES: In this study, we describe the pattern and characteristics of fatigue as a patient-reported adverse drug reaction (ADR) of biologics, and compared patient and treatment characteristics with patients reporting other ADRs or no ADRs. METHODS: In this cohort event monitoring study, the description and characteristics of fatigue reported as a possible ADR in the Dutch Biologic Monitor were assessed and analysed for commonly recurring themes or patterns. Baseline and treatment characteristics of patients with fatigue and patients reporting other ADRs or no ADRs were compared. RESULTS: Of 1382 participating patients, 108 patients (8%) reported fatigue as an ADR of a biologic. Almost half of these patients (50 patients, 46%) described episodes of fatigue during or shortly after biologic injection, which often recurred following subsequent injections. Patients with fatigue were significantly younger than patients with other ADRs or patients without ADRs (median age for patients with fatigue, 52 years; median age for patients with other ADRs, 56 years; and median age for patients without ADRs, 58 years); significantly more often smoked (25% vs. 16% and 15%); used infliximab (22% vs. 9% and 13%), rituximab (9% vs. 3% and 1%) or vedolizumab (6% vs. 2% and 1%); and significantly more often had Crohn's disease (28% vs. 13% and 13%) and other comorbidities (31% vs. 20% and 15%). Patients with fatigue significantly less frequently used etanercept (12% vs. 29% and 34%) or had rheumatoid arthritis (30% vs. 45% and 43%). CONCLUSIONS: IMID patients may experience fatigue as a postdosing effect of biologics.
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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 | 0.008 |
| 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 | 0.000 |
| 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".