Reliability and Validity of Two Versions of the Upper Extremity Functional Index
Bibliographic record
Abstract
PURPOSE: To examine the reliability, validity, and sensitivity to change of the 20-item version and the Rasch-refined 15-item version of the Upper Extremity Functional Index (UEFI-20 and UEFI-15, respectively) and to determine the impact of arm dominance on the positive minimal clinically important difference (pMCID). METHODS: Adults with upper-extremity (UE) dysfunction completed the UEFI-20, Upper Extremity Functional Scale (UEFS), Pain Limitation Scale, and Pain Intensity Scale at their initial physiotherapy assessment (Time 1); 24-48 hours later (Time 2); and 3 weeks into treatment or at discharge, whichever came first (Time 3). Demographics, including working status, were obtained at Time 1. Global ratings of change (GRC) were provided by the treating physiotherapist and patient at Time 3. The UEFI-15 was calculated from relevant items in the UEFI-20. The intra-class correlation coefficient (ICC) and minimal detectable change (MDC) quantified test-retest reliability (Time 1-Time 2). Cross-sectional convergent validity was determined by the association (Pearson's r) between Time 1 measures of function and pain. Known-groups validity was evaluated with a one-way ANOVA across three levels of working status. Longitudinal validity was determined by the association (Pearson's r) between function and pain change scores (Time 1-Time 3). Receiver operating characteristic (ROC) curves estimated the pMCID using Time 1-Time 3 change scores and average patient/therapist GRC. RESULTS: Reliability for the UEFI-20 and UEFI-15 was the same (ICC=0.94 for both measures). MDC values were 9.4/80 for the UEFI-20 and 8.8/100 for the UEFI-15. Cross-sectional, known-groups, and longitudinal validity were confirmed for both UEFI measures. pMCID values were 8/80 for the UEFI-20 and 6.7/100 for the UEFI-15; pMCID was higher for people whose non-dominant arm was affected. CONCLUSIONS: Both UEFI measures show acceptable reliability and validity. Arm dominance affects pMCID. The UEFI-15 is recommended because it measures only one dimension: UE function.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".