The Single-Leg-Stance Test in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Timed single-leg-stance test (SLST) is widely used to assess postural control in the elderly. In Parkinson's disease (PD), it has been shown that an SLST around 10 seconds or below may be a sensitive indicator of future falls. However, despite its role in fall risk, whether SLST times around 10 seconds marks a clinically important stage of disease progression has largely remained unexplored. METHODS: A cross-sectional study where 27 people with PD were recruited and instructed to undertake timed SLST for both legs was conducted. Disease motor impairment was assessed with the Unified Parkinson's Disease Rating Scale Part 3 (UPDRS-III). RESULTS: This study found that: 1) the SLST in people with PD shows good test-retest reliability; 2) SLST values can be attributed to two non-overlapping clusters: a low (10.4 ± 6.3 seconds) and a high (47.6 ± 11.7 seconds) value SLST group; 3) only the low value SLST group can be considered abnormal when age-matched normative SLST data are taken into account for comparison; and 4) lower UPDRS-III motor performance, and the bradykinesia sub-score in particular, are only associated with the low SLST group. CONCLUSION: These results lend further support that a low SLST time around 10 seconds marks a clinically important stage of disease progression with significant worsening of postural stability in PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".