Nonpharmacologic Interventions Need Outcomes for Evaluating Complex Interventions in Rheumatic Diseases
Bibliographic record
Abstract
INTRODUCTION: Nonpharmacologic interventions are themselves complex and are often combined with drugs and other interventions in the treatment of rheumatic diseases. Therefore, overall strategies for treatment are complex interventions. These should be evaluated regarding their processes and outcomes. METHODS: The CARE network, an international organization of health professionals (physicians and nonphysicians) and patients conducted a survey in 2008 to identify core outcomes in the ICF perspective, completed with a second survey (2009-2010) with patients in routine practice. These surveys have provided new information about domains to investigate as a basis for evaluating complex interventions. RESULTS: Outcome Measures in Rheumatology Clinical Trials (OMERACT) participants in this Special Interest Group agreed that current outcomes used in pharmacological research are not sufficient if the nonpharmacologic independent or combined contributions are to be assessed; other domains need to be addressed. This is an area of interest for further development. CONCLUSION: Recommendations are proposed to develop research in the area of outcome for evaluation of complex interventions in rheumatic diseases.
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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 | 0.071 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".