A Multidimensional Model of Fatigue in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate a multidimensional model testing disease activity, mood disturbance, and poor sleep quality as determinants of fatigue in patients with rheumatoid arthritis (RA). METHOD: The data of 106 participants were drawn from baseline of a randomized comparative efficacy trial of psychosocial interventions for RA. Sets of reliable and valid measures were used to represent model constructs. Structural equation modeling was used to test the direct effects of disease activity, mood disturbance, and poor sleep quality on fatigue, as well as the indirect effects of disease activity as mediated by mood disturbance and poor sleep quality. RESULTS: The final model fit the data well, and the specified predictors explained 62% of the variance in fatigue. Higher levels of disease activity, mood disturbance, and poor sleep quality had direct effects on fatigue. Disease activity was indirectly related to fatigue through its effects on mood disturbance, which in turn was related to poor sleep quality. Mood disturbance also indirectly influenced fatigue through poor sleep quality. CONCLUSION: Our findings confirmed the importance of a multidimensional framework in evaluating the contribution of disease activity, mood disturbance, and sleep quality to fatigue in RA using a structural equation approach. Mood disturbance and poor sleep quality played major roles in explaining fatigue along with patient-reported disease activity.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".