Psychometric Properties of Activity, Self-Efficacy, and Quality-of-Life Measures in Individuals with Parkinson Disease
Bibliographic record
Abstract
PURPOSE: To examine the psychometric properties of six outcome measures in people with Parkinson disease (PD). METHOD: Twenty-four participants completed the following twice within 2 weeks: the timed up-and-go test (TUG), Northwestern University Disability Scale (NUDS), Schwab & England ADL Scale (S&E), Activities-specific Balance Confidence (ABC) Scale, PD Questionnaire-Short Form (PDQ 8), and Stanford Self-Efficacy for Managing Chronic Disease 6-Item Scale (SSE). Internal consistency, test-retest reliability (ICC[3,1]), and minimal detectable change (MDC) scores were calculated. Convergent and discriminant validity of the ABC were examined. RESULTS: Cronbach's alpha scores for the NUDS, ABC, PDQ-8, and SSE were 0.47, 0.92, 0.72, and 0.91 respectively. The intra-class correlation coefficient (ICC[3,1]) for the TUG was 0.69 and could be improved by averaging two trials. ICCs for the NUDS, S&E, ABC, PDQ-8, and SSE were 0.56, 0.70, 0.79, 0.82, and 0.72 respectively. The ABC correlated with the TUG (r=-0.44, p=0.03) and with PDQ-8 (r(s)=0.51, p=0.01) and NUDS (r(s)=0.48, p=0.02) walking items. The ABC was able to discriminate between stages 1 and 3 of disease progression but not between stages 1 and 2, which suggests that the ABC can distinguish large differences in disease progression but cannot detect more subtle differences. CONCLUSIONS: Homogeneity of the ABC, PDQ-8, and SSE is good to excellent. Test-retest reliability scores of all measures except the NUDS are moderate to good. The ABC is a valid measure for use in PD. The MDC statistic may be useful for interpreting group score changes.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".