Incidence, prevalence, and predictors of inflammatory arthritis in patients with hidradenitis suppurativa: a systematic review and meta‐analysis
Bibliographic record
Abstract
An increasing amount of evidence has emerged suggesting that hidradenitis suppurativa (HS) is associated with inflammatory arthritis. This study reviewed the incidence, prevalence, and predictors of inflammatory arthritis in patients with HS. A comprehensive literature search was conducted in CINAHL, Embase, and Medline from inception to February 14, 2020. Articles were included in the review if they provided data on disease epidemiology or predictors of adult or pediatric HS patients with comorbid inflammatory arthritis. There are no validated diagnostic criteria for HS, thus we considered patients as having HS if they had at least one diagnostic code in a hospital or claims database or a diagnosis of HS/inflammatory arthritis in a medical record. The same criteria were used to confirm presence of inflammatory arthritis. We identified an increased incidence of rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriatic arthritis (PsA) in HS patients when compared with estimates in the general population. We identified a relatively high prevalence of RA, spondyloarthritis (SpA), and PsA in HS patients when compared with estimates in the general population. There was evidence to suggest that patients who are younger than 30, male, have severe HS, or are taking infliximab or adalimumab (which may also be confounded by HS disease severity) may be at greater risk for specific subtypes of inflammatory arthritis. However, further data are needed to confirm these associations. The increased incidence and prevalence of inflammatory arthritis within HS patients underscore the need for increased awareness and interdisciplinary partnership within rheumatology and dermatology.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".