The Problem of Pain in Systemic Lupus Erythematosus: An Explication of the Role of Biopsychosocial Mechanisms
Bibliographic record
Abstract
OBJECTIVE: To define biopsychosocial mechanisms of pain that go above and beyond disease activity and organ damage in systemic lupus erythematosus (SLE). METHODS: We conducted a cross-sectional analysis of patient-reported data in a population-based registry of 766 people with SLE. Predictors of pain intensity and interference were examined using hierarchical linear regression. We built 2 main hierarchical regression models with pain intensity and interference as outcomes, both regressed on disease activity and organ damage. For each model, we sought to establish the relationship between pain outcomes and the primary exposures using sequential steps comprising the inclusion of each construct in 6 stages: demographic, socioeconomic, physical, psychological, behavioral, and social factors. We also conducted sensitivity analyses eliminating all overt aspects of pain in the disease activity measure and reestimated the models. RESULTS: Disease activity and organ damage explained 32-33% of the variance in pain intensity and interference. Sociodemographic factors accounted for an additional 4-9% of variance in pain outcomes, whereas psychosocial/behavioral factors accounted for the final 4% of variance. In the sensitivity analyses, we found that disease activity and organ damage explained 25% of the variance in pain outcomes. CONCLUSION: Disease activity only explained 33% of the variance in pain outcomes. However, there was an attenuation in these associations after accounting for psychosocial/behavioral factors, highlighting their roles in modifying the relationship between disease activity and pain. These findings suggest that multilevel interventions may be needed to tackle the negative effect of pain in SLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".