Joint involvement in sarcoidosis: systematic review and meta-analysis of prevalence, clinical pattern and outcome
Bibliographic record
Abstract
OBJECTIVES: To characterize joint involvement (JI) in sarcoidosis, a systematic search of MEDLINE, EMBASE and Cochrane Library was conducted from inception to July 2022 for publications reporting its prevalence, pattern, treatment and outcome. METHODS: The pooled prevalence estimates (PPE) with 95% CI were calculated using binomial distribution and random effects. Meta-regression method was used to examine factors affecting heterogeneity between studies. RESULTS: Forty-nine articles were identified comprising a total of 8574 sarcoidosis patients, where 12% presented with JI (95% CI 10, 14; I2 = 0%). The PPE for sarcoid arthritis (SA) was 19% (95% CI 14, 24; I2 = 95%), and 32% (95% CI 13, 51; I2 = 99%) for arthralgia. Heterogeneity was due to higher JI prevalence reported in Western Asia and the Middle East, in rheumatology clinics and via surveys. Sample size of SA varied from 12 to 117 cases. Ankles were most frequently affected (PPE 80%) followed by knees and wrists. Monoarthritis was uncommon (PPE 1%; 95% CI 0, 2; I2 = 55%). Acute SA prevailed (PPE 79%; 95% CI 72, 88; I2 = 69%) with an equal proportion of oligo and polyarthritis and was frequently accompanied by erythema nodosum (PPE 62%; 95% CI 52, 71; I2 = 16%). Chronic SA was predominantly polyarticular with a higher frequency of the upper extremity joints affected. Most common non-articular manifestations with SA included fever (52%), erythema nodosum (41%), hilar adenopathy (86%) and interstitial lung disease (23%) of which one-third required corticosteroids and/or immunosuppressants. CONCLUSION: SA occurred early in the disease with a PPE of 19% and most frequent pattern of acute oligo- or polyarthritis predominantly affecting the lower extremity large joints.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".