Cognitive correlates of dual tasking costs on the timed up and go test in Parkinson disease
Bibliographic record
Abstract
Introduction: Dual tasking impairments are an increasingly recognized contributor to falls in Parkinson disease (PD) and may be a promising therapeutic target for PD fall prevention trials. Depending on the context, ambulatory dual tasking difficulties may be caused by different types of neurocognitive impairments. Methods: We performed a cross-sectional analysis of 21 participants with PD. All participants underwent detailed neuropsychological testing that was quantified using normative z-scores. All participants completed the 3-meter timed up and go test (TUG), with and without a dual tasking assignment. Biomechanistic properties of the TUG were quantified using APDM wearable OPAL sensors. We explored correlations between dual tasking cost (DTC) in 1) total TUG duration, 2) Sit-to-stand duration, 3) Stand-to-sit duration, and 4) turn velocity. Results: Impaired total DTC in the TUG correlated inversely with global cognitive performance measured using the Montreal Cognitive Assessment (MoCA) (r = -0.4649, p = 0.0337). Sit-to-stand DTC impairments correlated inversely with processing speed on the WAIS-IV Coding (r = -0.5762, p = 0.0063), semantic fluency (r = -0.5100, p = 0.0182) and learning and memory on the Hopkins Verbal Learning Test-Revised total recall (r = -0.5502, p = 0.0098). Impaired stand-to-sit DTC function corelated inversely with visuospatial cognitive function on the Benton Judgement of Line Orientation (JOLO) test (r = -0.5181, p = 0.0161). Conclusions: The link between dual tasking and fall risk in PD may be caused by cognitive features other than executive dysfunction and may vary based on the ambulatory task in question. These findings shed light on the cognitive contributions to falls 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".