OFF episode quality of life impact scale (OFFELIA): A new measure of quality of life for off episodes in Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: OFF Episodes occur in people with Parkinson's disease when their medication wears off, and motor and/or non-motor symptoms emerge. Existing measures used to assess OFF Episodes focus on the time spent in OFF Episodes through diaries or by identifying symptoms, but they are limited in their ability to capture the severity and functional impact of OFF episodes. The aim of this study was to develop and validate a new instrument, called "OFFELIA," that measures the impact of OFF episodes on the quality of life of individuals with Parkinson's disease. METHODS: Participants completed a cross-sectional questionnaire, "Impact and Communication on OFF Periods," while enrolled in the online clinical study Fox Insights. The data collected was used to develop OFFELIA. Psychometric testing was performed on 18 candidate items using classical, exploratory factor analysis, and item response theory methods. RESULTS: 569 individuals with Parkinson's disease completed the questionnaire. All items were retained for the final measure, with 17 items aggregated into two multi-item scales (functioning and psychological well-being) and one item reported separately as it did not function well with the other items (employment). Known group comparisons based on average duration, frequency and unpredictability of OFF episodes indicated that OFFELIA subscales were more sensitive than existing generic and condition-specific measures. CONCLUSION: Initial evidence supports the validity of OFFELIA, a new instrument that assesses the impact of OFF periods on daily life. This instrument can be used in assessing clinical therapeutic strategies targeting OFF episodes in Parkinson's disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".