Structural equation modelling provides insights to understand the construct of chronic pain in women with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: We aimed to adopt a multidimensional approach and investigate the interconnections between biomarkers (cytokines, matrix metalloproteinases, and cortisol) and psychosocial aspects considering pain acceptance, the individual construct of pain perception in terms of blood inflammation biomarkers, anxiety, self-efficacy, and functional performance and to define the quality of life (QoL) in women with rheumatoid arthritis (RA). METHODS: An observational cross-sectional study with a total of 42-RA participants, with chronic pain and 42-women without rheumatic diseases or chronic pain were included. A structural equation model was used to investigate the association between independent variables. RESULTS: Women with RA presented high blood biomarker levels, representing an intense inflammatory process. The participants with RA reported moderate pain most of the time, a worsening QoL, functionality, engagement in activities, and a willingness to live with pain and self-efficacy. It was found that the higher the chronic pain, the greater the intensity of pain perceived by these women with RA, as well as, the worse the functionality, the higher the perceived pain. CONCLUSIONS: The exacerbation of pain perception leads to worsening of the experience of chronic pain. The new construct of pain experience should include functionality as a crucial factor in understanding the mechanisms underlying pain.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".