Improved sleep efficiency after anti-tumor necrosis factor α therapy in rheumatoid arthritis patients
Bibliographic record
Abstract
BACKGROUND: Poor sleep health is increasingly recognized as contributing to decreased quality of life, increased morbidity/mortality and heightened pain perception. Our purpose in this study was to observe the effect on sleep parameters, specifically sleep efficiency, in rheumatoid arthritis (RA) patients treated with anti-tumor necrosis factor alpha (anti-TNF-α) therapy. METHODS: This was a prospective observational study of RA patients with hypersomnolence/poor sleep quality as defined by the Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI). Study patients underwent overnight polysomnograms and completed questionnaire instruments assessing sleep prior to starting anti-TNF-α therapy and again after being established on therapy. The questionnaire included the ESS, PSQI, the Berlin instrument for assessment of obstructive sleep apnea (OSA) risk, restless legs syndrome (RLS) diagnostic criteria, and measures of disease activity/impact. RESULTS: A total of 12 RA patients met inclusion criteria, of which 10 initiated anti-TNF-α therapy and underwent repeat polysomnograms and questionnaire studies approximately 2 months later. Polysomnographic criteria for OSA were met by 60% of patients. Following anti-TNF-α therapy initiation, significant improvements were observed by polysomnography (PSG) for sleep efficiency, increasing from 73.9% (SD 13.5) to 85.4% (SD 9.6) (p = 0.031), and 'awakening after sleep onset' time, decreasing from 84.1 minutes (SD 43.2) to 50.7 minutes (SD 36.5) (p = 0.048). Questionnaire instrument improvements were apparent in pain, fatigue, modified Health Assessment Questionnaire (mHAQ), and Rheumatoid Arthritis Disease Activity Index (RADAI) scores. CONCLUSIONS: Improved sleep efficiency and 'awakening after sleep onset' time were observed in RA patients treated with anti-TNF-α therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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 teacher head, 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".