Relationship between sleep disorders, pain and C-reactive protein in patients with rheumatoid arthritis
Bibliographic record
Abstract
Objective To study the relationship between sleep disorders, pain and C-reactive protein in patients with rheumatoid arthritis. Methods A total of 115 patients with rheumatoid arthritis admitted to the Department of Rheumatology and Immunology of the hospital from March 2014 to March 2016 were selected by convenient sampling. The demographics of each patient were recorded. Each selected patient was assessed for sleep disorder by the Sleep Disorders Questionnaire (SDQ). Pain assessment was performed for each patient using a simplified McGill pain score. At the time of admission, our department nurses was responsible for blood collection and C-reactive protein (CRP), and >10 mg/L was positive for CRP. The 37 questions of sleep disorders were stratified, and each stratification variable was correlated with pain scores. Each patient demographic variable, sleep disorder questionnaire score, pain score, and CRP adjusted for CRP were used to correct the disordered multi-class Logistic regression analysis. Results All patients had a sleep score of 21.2±10.3 and a pain score of 5.4±3.6. There were 9 items in the sleep disorder that were statistically significantly associated with pain scores, including apnea, sleep hypopnea, difficulty falling asleep, limb convulsions during sleep, limb numbness during sleep, nightmares during sleep, wakefulness during sleep, snoring during sleep Pain was significantly positively correlated (r = 0.22-0.57, P < 0.01). Diabetes, hypertension, sleep disturbance, and pain in the regression analysis were independent factors of CRP (β=0.21-0.33, P<0.05). Conclusions Sleep disorders and pain in patients with rheumatoid arthritis can increase CRP and aggravate the disease. The care process requires special attention to patients with sleep disorders and pain. Key words: Arthritis; Rheumatoid diseases; C-reactive protein
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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 | 0.002 |
| 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 | 0.001 |
| 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".