Rasch analysis of the Patient Rated Elbow Evaluation questionnaire
Bibliographic record
Abstract
BACKGROUND: The Patient Rated Elbow Evaluation (PREE) was developed as an elbow joint specific measure of pain and disability and validated with classical psychometric methods. More recently, Rasch analysis has contributed new methods for analyzing the clinical measurement properties of self-report outcome measures. The objective of the study was to determine aspects of validity of the PREE using the Rasch model to assess the overall fit of the PREE data, the response scaling, individual item fit, differential item functioning (DIF), local dependency, unidimensionality and person separation index (PSI). METHODS: A convenience sample of 236 patients (Age range 21-79 years; M: F- 97:139) with elbow disorders were recruited from the Roth│McFarlane Hand and Upper Limb Centre, London, Ontario, Canada. The baseline scores of the PREE were used. Rasch analysis was conducted using RUMM 2030 software on the 3 sub scales of the PREE separately. RESULTS: The 3 sub scales showed misfit initially with disordered thresholds on17 out of 20 items), uniform DIF was observed for two items ("Carrying a 10lbs object" from specific activities subscale for age group; and "household work" from the usual activities subscale for gender); multidimensionality and local dependency. The Pain subscale satisfied Rasch expectations when item 2 "Pain - At rest" was split for age group, while the usual activities subscale readily stood up to Rasch requirements when the item 2 "household work" was split for gender. The specific activities subscale demonstrated fit to the Rasch model when sub test analysis accounted for local dependency. All three subscales of the PREE were well targeted and had high reliability (PSI >0.80). CONCLUSION: The three subscales of the PREE appear to be robust when tested against the Rasch model when subject to a few alterations. The value of changing the 0-10 format is questionable given its widespread use; further Rasch-based analysis of whether these findings are stable in other samples is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".