Increased incidence and prevalence of psoriasis in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Psoriasis and multiple sclerosis (MS) share some risk factors, and fumarates are effective disease-modifying therapies for both psoriasis and MS, suggesting a common pathogenesis. However, findings regarding the occurrence of psoriasis in the MS population are inconsistent. OBJECTIVES: We aimed to estimate the incidence and prevalence of psoriasis in the MS population versus a matched cohort from the general population. METHODS: We used population-based administrative data from the Canadian province of Manitoba to identify 4911 persons with MS and 23,274 age-, sex- and geographically-matched controls aged 20 years and older. We developed case definitions for psoriasis using ICD-9/10 codes and prescription claims. These case definitions were compared to self-reported psoriasis diagnoses. The preferred definition was applied to estimate the incidence and prevalence of psoriasis over the period 1998-2008. We used multivariable Cox regression to estimate the risk of psoriasis in the MS population at the individual level, adjusting for sex, age at the index date, socioeconomic status and physician visits. RESULTS: In 2008, the crude incidence of psoriasis per 100,000 person-years was 466.7 (95%CI: 266.8-758.0) in the MS population, and 221.3 in the matched population (95%CI: 158.1-301.4). The crude prevalence of psoriasis per 100,000 persons was 4666.1 (95%CI: 3985.2-5429.9) in the MS population, and 3313.5 (95%CI: 3057.4-3585.3) in the matched population. The incidence and prevalence of psoriasis rose slightly over time. After adjusting for sex, age at the index date, socioeconomic status and physician visits, the risk of incident psoriasis was 54% higher in the MS population (HR 1.54; 95%CI: 1.07-2.24). CONCLUSION: Psoriasis incidence and prevalence are higher in the MS population than in the matched population.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".