A Systematic Literature Review on the Application of Rasch Analysis in Musculoskeletal Disease — A Special Interest Group Report of OMERACT 11
Bibliographic record
Abstract
OBJECTIVE: The Rasch measurement model provides robust analysis of the internal construct validity of outcome measures. We reviewed the application of Rasch analysis in musculoskeletal medicine as part of the work leading to discussion in a Special Interest Group in Rasch Analysis at Outcome Measures in Rheumatology 11. METHODS: A systematic literature review of SCOPUS and MEDLINE was performed (January 1, 1985, to February 29, 2012. Original research reports in English using "Rasch" or "Item Response Theory" in musculoskeletal diseases were assessed by 2 independent reviewers. The topics of focus and analysis methodology details were recorded. RESULTS: Of 212 articles reviewed, 114 were included. The number of publications rose from 1 in 1991-1992 to 23 in 2011-February 2012. Disease areas included rheumatoid arthritis (28%), osteoarthritis (16.6%), and general musculoskeletal disorders (43%). Sixty-six reports (57.9%) evaluated psychometric properties of existing scales and 35 (30.7%) involved development of new scales. Nine articles (7.9%) were on methodology illustration. Four articles were on item banking and computer adaptive testing. A majority of the articles reported fit statistics, while the basic Rasch model assumption (i.e., unidimensionality) was examined in only 57.2% of the articles. An improvement in reporting qualities with Rasch articles was noted over time. In addition, only 11.4% of the articles provided a transformation table for interval scale measurement in clinical practice. CONCLUSION: The Rasch model has been increasingly used in rheumatology over the last 2 decades in a wide range of applications. The majority of the articles demonstrated reasonable quality of reporting. Improvements in quality of reporting over time were revealed.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.032 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".