Design and validation of a new instrument to assess fear of falling in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Fear of falling may be significantly associated with falls in Parkinson's disease (PD) and may have a negative impact on quality of life. Nevertheless, there are no valid and reliable tools to examine this condition in PD. The objective of this study was to design and determine the psychometric attributes of an instrument to assess fear of falling in PD. METHODS: A prospective 1-year, 2-phase study was conducted to validate the Fear of Falling Scale, a self-assessed instrument for assessing fear of falling in PD. During phase 1, we designed a scale to measure the severity of fear of falling and determine its baseline psychometric characteristics, whereas phase 2 was a 1-year follow-up study to assess the frequency of falls and other clinical factors linked to fear of falling. Convergent and discriminant validity were assessed against the Fear of Falling Measure and the Starkstein Apathy Scale, respectively. RESULTS: The Fear of Falling Scale showed high internal consistency, test-retest reliability, and strong convergent and discriminant validity. There was a significant association between fear of falling score and the presence of both generalized anxiety disorder and major depression, poor balance-related motor ability, increased nonmotor symptoms of PD, more severe impairments in activities of daily living, and increased motor fluctuations. Finally, generalized anxiety disorder was a significant predictor of number of falls during a 12-month follow-up period. CONCLUSIONS: The Fear of Falling Scale is a valid and reliable instrument to assess fear of falling in PD. Fear of falling in PD is associated with specific psychiatric and motor disorders and is significantly related to the performance of balance-related motor functions. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".