Consensus Statement on the Management of Comorbidity in Patients with Rheumatoid Arthritis and Psoriasis
Bibliographic record
Abstract
To the Editor: We have read with great interest the recommendations for the management of comorbidities in rheumatoid arthritis (RA), psoriasis (PsO), and psoriatic arthritis (PsA) made by the Canadian Dermatology-Rheumatology Comorbidity Initiative1. The work is very interesting and comprehensive, and we congratulate the authors for combining more general recommendations for the management of the comorbidity of these 3 diseases. Indeed, it is sometimes difficult to distinguish between comorbidities, risk factors, medication adverse events, and extraarticular manifestations. But it holds clear that the presence of several different diseases aggravates the monitoring of these patients. We have recently published a consensus statement for the management of comorbidity and extraarticular manifestations in RA2 and a practical derivation algorithm of patients with comorbidity associated with PsO in Spain3. In the mentioned manuscript, the Canadian experts selected 8 main topics regarding comorbidities in RA, PsA, and PsO1. The Spanish panel selected the 10 most relevant comorbidities and risk factors based on a ranking depending on incidence, mortality, and preventability. Interestingly, 8 … Address correspondence to Dr. S. Castañeda, Rheumatology Department, Hospital de La Princesa, IIS-IPrincesa, Universidad Autónoma de Madrid, C/ Diego de Leon 62, 28006-Madrid, Spain. E-mail: scastas{at}gmail.com, santos.castaneda{at}salud.madrid.org
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".