Patients with rheumatoid arthritis presenting with mono- or oligo-arthritis and high VAS-ratings remain the most fatigued during 5 years of follow-up
Bibliographic record
Abstract
OBJECTIVES: The severity of fatigue in RA has improved very little in recent decades, leaving a large unmet need. Fortunately, not all RA patients suffer from persistent fatigue, but the subgroup of patients who suffer the most is insufficiently recognizable at diagnosis. As disease activity is partly coupled to fatigue, DAS components may associate with the course of fatigue. We aimed to identify those RA patients who remain fatigued by studying DAS components at diagnosis in relation to the course of fatigue over a 5-year follow-up period in two independent early RA cohorts. METHODS: In all, 1560 consecutive RA patients included in the Leiden Early Arthritis Cohort and 415 RA patients included in the tREACH trial were studied. Swollen joint count, tender joint count, ESR and Patient Global Assessment (PGA) [on a Visual Analogue Scale (VAS)] were studied in relation to fatigue (VAS, 0-100 mm) over a period of 5 years, using linear mixed models. RESULTS: Higher tender joint count and higher PGA at diagnosis were associated with a more severe course of fatigue. Furthermore, patients with mono- or oligo-arthritis at diagnosis remained more fatigued. The swollen joint count, in contrast, showed an inverse association. An investigation of combinations of the aforementioned characteristics revealed that patients presenting with mono- or oligo-arthritis and PGA ≥ 50 remained the most fatigued over time (+20 mm vs polyarthritis with PGA < 50), while the DAS course over time did not differ. This subgroup comprised 14% of the early RA population. Data from the tREACH trial showed similar findings. CONCLUSION: The RA patients who remain the most fatigued were those characterized by mono- or oligo-arthritis and high PGA (VAS ≥ 50) at diagnosis. This understanding may enable early-intervention with non-pharmacological approaches in dedicated patient groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".