The Relationship Between Function and Disease Activity as Measured by the HAQ and DAS28 Varies Over Time and by Rheumatoid Factor Status in Early Inflammatory Arthritis (EIA). Results from the CATCH Cohort§
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between function and disease activity in early inflammatory arthritis (EIA). METHODS: Canadian Early Arthritis Cohort (CATCH) (n=1143) is a multi-site EIA cohort. Correlations between the Health Assessment Questionnaire Disability Index (HAQ) and DAS28 were done at every 3 months for the first year and then at 18 and 24 months. We also investigated the relationship between HAQ and DAS28 by age (<65 versus ≥65) and RF (positive vs negative). RESULTS: Mean HAQ and DAS28 scores were highest at the initial visit with HAQ decreasing over 24 months from a baseline of 0.94 to 0.40 and DAS28 scores decreasing from 4.54 to 2.29. All correlations between HAQ and DAS28 were significant at all time points (p<0.01). The correlations between HAQ and DAS28 were variable over time. The strongest correlation between HAQ and DAS28 occurred at initial visit (most DMARD naive) (n=1,143) and 18 months (r=0.57, n=321) and 24 months (r=0.59, n=214). The baseline correlation between HAQ and DAS28 was significantly different than correlations obtained at 3, 6, and 12 months (p=0.02, 0.01, and 0.01, respectively). Age did not change the association between HAQ and DAS28 {<65 years old (r=0.50, n=868) versus ≥65 (r=0.48, n=254), p=0.49}. The correlation between HAQ and DAS28 was stronger with RF+ patients (r=0.63, n=636) vs RF negative (r=0.47, n=477), p=0.0043. CONCLUSION: Over 2 years in EIA, HAQ and DAS both improved; correlations at time points were different over 2 years and RF status affected the correlations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".