Mapping of the Outcome Measures in Rheumatology Core Set for Antineutrophil Cytoplasmic Antibody‐Associated Vasculitis to the International Classification of Function, Disability and Health
Bibliographic record
Abstract
OBJECTIVE: The International Classification of Functioning, Disability and Health (ICF) is a framework and classification of health that describes health along 4 components: body functions, body structures, activities and participation, and contextual factors. This study examined the content of instruments that constitute the Outcome Measures in Rheumatology (OMERACT) core set of outcome measures for antineutrophil cytoplasmic antibody-associated vasculitis (AAV) by "mapping" them to the ICF. METHODS: The content of the instruments included in the AAV core set were linked to the ICF by 2 independent investigators according to previously established ICF linkage rules. RESULTS: The AAV core set includes 3 measures of disease activity (3 versions of the Birmingham Vasculitis Activity Score), 1 damage measure (Vasculitis Damage Index), 1 patient-reported outcome (Short Form 36 health survey), and death. Linking these instruments to the ICF revealed comprehensive coverage of the ICF components body functions and body structures, limited coverage of the ICF component activities and participation, and complete absence of coverage of contextual factors. CONCLUSION: ICF was found to be useful for thematic characterization of a heterogeneous group of outcome measures for AAV, i.e., a group of complex medical conditions. Linking of the instruments selected for the OMERACT AAV core set of outcome measures to the ICF classification revealed limitations in the representation of constructs related to life impact of AAV, represented by the ICF components activities and participation and contextual factors. Further research and methods development are needed to better incorporate important aspects of functioning and health relevant to patients into clinical trials of AAV.
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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 | 0.029 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".