Physician global assessment at 3 months is strongly predictive of remission at 12 months in early rheumatoid arthritis: results from the CATCH cohort
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine predictors of 1-year remission in early RA (ERA) using baseline and 3-month data. METHODS: The Canadian Early Arthritis Cohort (CATCH) patients were included if baseline, 3- and 12-month data were available. Regression analyses for four different definitions of remission at 12 months were done to determine baseline and 3-month predictors of remission. RESULTS: Five hundred and sixty-two patients had complete data at 12 months (mean age 53.4 years, disease duration 6.2 years, 73% female). The factors at baseline associated with all four remission outcomes at 12 months were age, gender, income, education, tender joint count (TJC), patient global assessment (PtGA), HAQ and pain. Baseline ESR was associated with the 28-joint DAS (DAS28) remission only. At 3 months, all four remission definitions were associated with TJC, swollen joint count, physician global assessment (PGA), PtGA, HAQ, pain, ESR and CRP in univariate analyses. In the regression model, variables associated with Simple Disease Activity Index remission were PGA [odds ratio (OR) 0.77, P < 0.001), pain (OR 0.85, P = 0.004), age (OR 0.98, P = 0.006) and HAQ (OR 0.49, P = 0.011); Clinical Disease Activity Index remission was associated with PGA (OR 0.77, P < 0.001), pain (OR 0.85, P = 0.003), age (OR 0.98, P = 0.015) and CRP (OR 0.80, P = 0.031). DAS28 remission was predicted by ESR (OR 0.95, P < 0.001), PGA (OR 0.76, P < 0.001), age (OR 0.98, P = 0.001), HAQ (OR 0.57, P = 0.006) and male gender (OR 2.01, P = 0.005), whereas Boolean remission was associated with pain (OR 0.79, P = 0.009), age (OR 0.98, P = 0.016), PtGA (OR 0.83, P = 0.025) and PGA (OR 0.86, P = 0.038). CONCLUSION: A low PGA at 3 months was consistently associated with 1-year remission in ERA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".