More Than Pain and Stiffness: Persistent Fatigue and Sleep Disturbance in Polymyalgia Rheumatica
Bibliographic record
Abstract
OBJECTIVE: We aimed to examine fatigue and sleep disturbance in patients with recently diagnosed polymyalgia rheumatica (PMR) compared to age- and gender-matched controls, including associated characteristics and change over an 18-month follow-up period. METHODS: Patients meeting the 2012 European Alliance of Associations for Rheumatology/American College of Rheumatology classification criteria for PMR were prospectively recruited, together with matched controls. Assessments were undertaken 3 months after the commencement of glucocorticoids and again 18 months later. Fatigue was quantified using the Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire and the 36-item Short Form Health Survey vitality scale. Sleep was quantified using the Pittsburgh Sleep Quality Index. Other data collected included PMR disease activity, depression, anxiety, and physical function status. All participants underwent body composition by dual-energy x-ray absorptiometry and physical function testing. RESULTS: Thirty-six participants with PMR and 32 controls were included. PMR disease activity was low at both visits. Significantly, more patients with PMR than controls suffered severe fatigue (PMR: 36% and 35% at baseline and follow-up, respectively; controls: 3% at both timepoints). Poor sleep quality also affected more patients with PMR (77% and 84% at baseline and follow-up, respectively) than controls (56% at both timepoints). Higher BMI and fat mass index, anxiety, depression, PMR Activity Score, inflammatory markers, pain, and stiffness were all associated with severe fatigue. There were no significant associations with poor sleep. CONCLUSION: Patients with PMR experience a disproportionate degree of fatigue and sleep disturbance, which persists almost 2 years after starting treatment. Features associated with fatigue include higher adiposity, psychological comorbidity, and PMR 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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".