Development of EULAR recommendations for the reporting of clinical trial extension studies in rheumatology
Bibliographic record
Abstract
OBJECTIVES: Our initiative aimed to produce recommendations on post-randomised controlled trial (RCT) trial extension studies (TES) reporting using European League Against Rheumatism (EULAR) standard operating procedures in order to achieve more meaningful output and standardisation of reports. METHODS: We formed a task force of 22 participants comprising RCT experts, clinical epidemiologists and patient representatives. A two-stage Delphi survey was conducted to discuss the domains of evaluation of a TES and definitions. A '0-10' agreement scale assessed each domain and definition. The resulting set of recommendations was further refined and a final vote taken for task force acceptance. RESULTS: Seven key domains and individual components were evaluated and led to agreed recommendations including definition of a TES (100% agreement), minimal data necessary (100% agreement), method of data analysis (agreement mean (SD) scores ranging between 7.9 (0.84) and 9.0 (2.16)) and reporting of results as well as ethical issues. Key recommendations included reporting of absolute numbers at each stage from the RCT to TES with reasons given for drop-out at each stage, and inclusion of a flowchart detailing change in numbers at each stage and focus (mean (SD) agreement 9.9 (0.36)). A final vote accepted the set of recommendations. CONCLUSIONS: This EULAR task force provides recommendations for implementation in future TES to ensure a standardised approach to reporting. Use of this document should provide the rheumatology community with a more accurate and meaningful output from future TES, enabling better understanding and more confident application in clinical practice towards improving patient 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 | 0.817 | 0.816 |
| Meta-epidemiology (narrow) | 0.006 | 0.012 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.029 | 0.024 |
| Research integrity | 0.049 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".