Does achieving clinical response prevent work stoppage or work absence among employed patients with early rheumatoid arthritis?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of clinical response on work stoppage or work absence among employed people with early RA. METHODS: First-year data from the combination of MTX and etanercept trial was used. The analyses were restricted to the 205 patients working full or part time at baseline who answered questions on whether they stopped working or missed days from work in one or more of the four follow-up visits. Work stoppage referred to the first occurrence of subjects reporting stopping work. Work absence was defined as whether patients reported missed days from work. Clinical response and activity state considered included the ACR and European League against Rheumatism response criteria, 28-joint DAS (DAS-28) remission and the minimum clinically important difference of the HAQ score. RESULTS: After adjustment for baseline characteristics, ACR70 responders were 72% less likely to stop working and 55% less likely to miss work than ACR20 non-responders (P < 0.05). Patients achieving DAS-28 remission were 54% less likely to stop work than those with DAS-28 > 3.2 (P < 0.05). Moderate improvements did not appear to effect work stoppage or missed days after adjustments. CONCLUSIONS: Results suggest that achieving clinical remission or major improvement might be necessary to significantly impact work 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".