EULAR Points to Consider (PtC) for designing, analysing and reporting of studies with work participation as an outcome domain in patients with inflammatory arthritis
Bibliographic record
Abstract
BACKGROUND: Clinical studies with work participation (WP) as an outcome domain pose particular methodological challenges that hamper interpretation, comparison between studies and meta-analyses. OBJECTIVES: To develop Points to Consider (PtC) for design, analysis and reporting of studies of patients with inflammatory arthritis that include WP as a primary or secondary outcome domain. METHODS: The EULAR Standardised Operating Procedures were followed. A multidisciplinary taskforce with 22 experts including patients with rheumatic diseases, from 10 EULAR countries and Canada, identified methodologic areas of concern. Two systematic literature reviews (SLR) appraised the methodology across these areas. In parallel, two surveys among professional societies and experts outside the taskforce sought for additional methodological areas or existing conducting/reporting recommendations. The taskforce formulated the PtC after presentation of the SLRs and survey results, and discussion. Consensus was obtained through informal voting, with levels of agreement obtained anonymously. RESULTS: Two overarching principles and nine PtC were formulated. The taskforce recommends to align the work-related study objective to the design, duration, and outcome domains/measurement instruments of the study (PtC: 1-3); to identify contextual factors upfront and account for them in analyses (PtC: 4); to account for interdependence of different work outcome domains and for changes in work status over time (PtC: 5-7); to present results as means as well as proportions of patients reaching predefined meaningful categories (PtC: 8) and to explicitly report volumes of productivity loss when costs are an outcome (PtC:9). CONCLUSION: Adherence to these EULAR PtC will improve the methodological quality of studies evaluating WP.
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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 | 0.781 | 0.749 |
| Meta-epidemiology (narrow) | 0.007 | 0.011 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.017 | 0.029 |
| Research integrity | 0.037 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".