Chronic Pain and Assessment of Pain Sensitivity in Patients With Axial Spondyloarthritis: Results From the SPARTAKUS Cohort
Bibliographic record
Abstract
OBJECTIVE: To study differences in pain reports between patients with ankylosing spondylitis (AS) and nonradiographic axial spondyloarthritis (nr-axSpA), and to assess how pain sensitivity measures associate with disease and health outcomes. METHODS: Consecutive patients with axial SpA (axSpA) were enrolled in the population-based SPARTAKUS cohort (2015-2017) and classified as AS (n = 120) or nr-axSpA (n = 55). Pain was assessed with questionnaires (intensity/duration/distribution) and computerized cuff pressure algometry to measure pain sensitivity (pain threshold/pain tolerance/temporal summation of pain). Linear regression models were used to compare pain measures between patients with AS and nr-axSpA, and to assess associations between pain sensitivity measures and disease and health outcomes. RESULTS: Of 175 patients with axSpA, 43% reported chronic widespread pain, with no significant differences in any questionnaire-derived or algometry-assessed pain measures between patients with AS and nr-axSpA. Lower pain tolerance was associated with longer symptom duration, worse Ankylosing Spondylitis Disease Activity Score using C-reactive protein (ASDAS-CRP), Bath Ankylosing Spondylitis Functional Index, and Bath Ankylosing Spondylitis Metrology Index (BASMI), more pain regions, unacceptable pain, worse Maastricht AS Enthesitis Score (MASES), fatigue, anxiety, and health-related quality of life. Further, lower pain threshold was associated with worse ASDAS-CRP and MASES, whereas higher temporal summation was associated with longer symptom duration, unacceptable pain, and worse BASMI. CONCLUSION: Chronic pain is common in axSpA, with no observed differences in any pain measures between patients with AS and nr-axSpA. Further, higher pain sensitivity is associated with having worse disease and health outcomes. The results indicate that patients with AS and nr-axSpA, in line with most clinical characteristics, have a similar pain burden, and they highlight large unmet needs regarding individualized pain management, regardless of axSpA subgroup.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".