Degenerative Disc Disease in Young Adults With Psoriatic Arthritis
Bibliographic record
Abstract
Objective We aimed to explore the prevalence of degenerative disc disease (DDD) in patients with psoriatic arthritis (PsA) aged < 50 years and to describe the factors associated with its development. We also examined the association between radiographic axial imaging findings and inflammatory back pain (IBP) and mechanical back pain. Methods We included patients with PsA aged < 50 years who were followed at our prospective observational cohort. We defined DDD as intervertebral disc space narrowing, spur formation, facet joint arthrosis, and spondylolisthesis on anteroposterior and lateral plain radiographs of the cervical and thoracolumbar spine. To identify factors associated with the development of DDD, we used multivariate Cox regression analysis. We used generalized estimating equations (GEEs) to test the association between imaging findings (isolated DDD, isolated axial disease, and both) and the type of back pain. Results Of 814 patients included in the study, 316 (38.8%) were observed to have DDD on plain radiographs of the spine. Factors associated with the development of DDD included older age (hazard ratio [HR] 1.08, P < 0.01), male sex (HR 1.52, P = 0.03), diabetes mellitus (HR 2.35, P = 0.045), and IBP (HR 2.03, P < 0.01). Being employed (vs unemployed), higher BMI, calcaneal spurs, and targeted disease-modifying antirheumatic drug use showed a trending association with DDD. In the GEE analysis, none of the abnormal imaging findings were significantly associated with back pain or IBP. Conclusion DDD is common in young patients with PsA, and its development may be associated with demographic features, comorbidities, and disease-related factors. IBP does not reliably distinguish between axial PsA and DDD.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".