Association of Higher Levels of High‐Sensitivity C‐Reactive Protein With Future Development of Psoriatic Arthritis in Psoriasis: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess whether high-sensitivity C-reactive protein (hsCRP) levels could predict the development of psoriatic arthritis (PsA) in patients with psoriasis. METHODS: We analyzed data from a prospective cohort of patients with psoriasis without PsA at enrollment. Participants were assessed annually by a rheumatologist for signs and symptoms of PsA. Information on patient demographics, psoriasis features, medications and musculoskeletal symptoms was collected. hsCRP levels were measured in serum samples collected at baseline using standard commercial assays. The association between hsCRP levels and risk of development of PsA was assessed using multivariable Cox proportional hazards model adjusted for age, sex, psoriasis severity and duration, nail lesions, body mass index (BMI), fatigue, and medication use. RESULTS: A total of 589 patients with psoriasis observed from 2006 to 2019 were analyzed. During the follow up period, 57 patients developed PsA. The mean level of hsCRP was 3.1 ± 5.5 mg/L (hsCRP levels in patients with incident PsA, 5.4 ± 13.1 mg/L). Significantly higher levels of hsCRP at baseline were found in patients with arthralgia, obesity, and in women. Higher hsCRP levels were associated with future development of PsA in multivariable analyses (hazard ratio 1.04; 95% confidence interval 1.01-1.07; P = 0.007). Similar effect size was seen in men and women. No significant interaction was found between hsCRP and sex or BMI. CONCLUSION: Higher levels of systemic inflammation, as measured by hsCRP levels, are associated with future development of PsA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".