Palindromic Rheumatism Frequently Precedes Early Rheumatoid Arthritis: Results From an Incident Cohort
Bibliographic record
Abstract
Background This multicenter incident cohort aimed to characterize how often early rheumatoid arthritis (ERA) patients self‐report episodic joint inflammation (palindromic rheumatism) preceding ERA diagnosis and which characteristics differentiate these patients from those without prior episodic symptoms. Methods Data were from patients with early confirmed or suspected RA (more than 6 weeks and less than 12 months) enrolled in the Canadian Early ArThritis CoHort (CATCH) between April 2017 to March 2018 who completed study case report forms assessing joint pain and swelling prior to ERA diagnosis. Chi‐square and t tests were used to compare characteristics of patients with and without self‐reported episodic joint inflammation prior to ERA diagnosis. Multivariable logistic regression was used to identify sociodemographic and clinical measures associated with past episodic joint inflammation around the time of ERA diagnosis. Results A total of 154 ERA patients were included; 66% were female, and mean (SD) age and RA symptom duration were 54 (15) years and 141 (118) days. Sixty‐five (42%) ERA patients reported a history of episodic joint pain and swelling, half of whom reported that these symptoms preceded ERA diagnosis by over 6 months. ERA patients with past episodic joint inflammation were more often female, had higher income, were seropositive, had more comorbidities, fewer swollen joints, and lower Clinical Disease Activity Index (CDAI) around the time of ERA diagnosis (P < 0.05). These associations remained significant in multivariable regression adjusting for other sociodemographic and RA clinical measures. Conclusion Almost half of ERA patients experienced episodic joint inflammation prior to ERA diagnosis. These patients were more often female, had higher income, and presented with milder disease activity at ERA diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".