Evidence-based Recommendations for the Management of Comorbidities in Rheumatoid Arthritis, Psoriasis, and Psoriatic Arthritis: Expert Opinion of the Canadian Dermatology-Rheumatology Comorbidity Initiative
Bibliographic record
Abstract
OBJECTIVE: Comorbidities such as cardiovascular diseases (CVD), cancer, osteoporosis, and depression are often underrecognized in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), or psoriasis (PsO). Recommendations may improve identification and treatment of comorbidities. The Canadian Dermatology-Rheumatology Comorbidity Initiative reviewed the literature to develop practical evidence-based recommendations for management of comorbidities in patients with RA, PsA, and PsO. METHODS: Eight main topics regarding comorbidities in RA, PsA, and PsO were developed. MEDLINE, EMBASE, and the Cochrane Library (1960-12/2012), together with abstracts from major rheumatology and dermatology congresses (2010-2012), were searched for relevant publications. Selected articles were analyzed and metaanalyses performed whenever possible. A meeting including rheumatologists, dermatologists, trainees/fellows, and invited experts was held to develop consensus-based recommendations using a Delphi process with prespecified cutoff agreement. Level of agreement was measured using a 10-point Likert scale (1 = no agreement, 10 = full agreement) and the potential effect of recommendations on daily clinical practice was considered. Grade of recommendation (ranging from A to D) was determined according to the Oxford Centre for Evidence-Based Medicine evidence levels. RESULTS: A total of 17,575 articles were identified, of which 407 were reviewed. Recommendations were synthesized into 19 final recommendations ranging mainly from grade C to D, and relating to a large spectrum of comorbidities observed in clinical practice: CVD, obesity, osteoporosis, depression, infections, and cancer. Level of agreement ranged from 80.9% to 95.8%. CONCLUSION: These practical evidence-based recommendations can guide management of comorbidities in patients with RA, PsA, and PsO and optimize outcomes.
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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 | 0.053 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".