Predictors of Pain for Patients With Early Inflammatory Polyarthritis
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of pain at 1 year in patients with early inflammatory polyarthritis (EIP). METHODS: Using a prospective design, patients were examined by a rheumatologist and completed questionnaires at baseline and at 1 year after symptom onset. Separate regression analyses were run for pain intensity, sensory pain, and affective pain. Age and sex were adjusted in cross-sectional and longitudinal analyses; baseline potential predictors consisted of measures for corresponding pain values and disease activity, depression, coping scores, medication use, rheumatoid arthritis criteria being met, and duration of symptoms. RESULTS: A total of 211 patients were enrolled in the study (mean ± SD age 58.8 ± 14.2 years, 63% women). There were significant improvements at 1 year for disease activity, instrumental coping, emotional coping, depression, and all 3 pain measures. At baseline, disease activity and depression were positively associated with all types of pain; in addition, instrumental coping was positively associated with sensory pain and palliative coping was positively associated with affective pain. At 1 year, pain intensity was predicted by baseline pain intensity, duration of symptoms, use of disease-modifying antirheumatic drugs (DMARDs), and emotional coping. Sensory pain was predicted by baseline sensory pain and DMARD use. Affective pain was predicted by baseline affective pain, DMARD use, and emotional coping. CONCLUSION: The majority of treated EIP patients can expect improvements in clinical and psychosocial variables over the first year of their illness. Emotional coping at baseline may contribute to pain in the future, and therefore it may be useful for patients to learn other means of dealing with this chronic disease.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".