Performance fatigability on a constant-load dynamic task is greater in females with moderate-severity Parkinson’s disease than healthy-aging controls
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder associated with self-reported fatigue. While fatigue is a disabling symptom, performance fatigability, a decline in strength or power during sustained tasks, remains poorly understood, particularly during isotonic (constant-load) contractions, which are encountered in everyday activities. We assessed performance fatigability and recovery in people with PD compared to healthy-aging controls. Fifteen females with PD and 14 healthy-aging controls underwent neuromuscular testing of knee extensors using dynamometry. Participants then performed repeated maximal-effort isotonic knee extensions at a 20 % maximum voluntary contraction (MVC) load until peak power declined 40 %. Performance fatigability was assessed by repetitions to task failure, with recovery tracked for 10 min afterwards. Despite no baseline differences in neuromuscular performance, PD achieved ∼58 % fewer repetitions to failure. Immediately post-task failure, compared to controls, PD exhibited less fatigue-induced impairments in MVC torque, voluntary activation, and quadriceps electromyographic (EMG) activity, but similar impairment of twitch torque. For power and twitch torque, PD and controls recovered similarly, whereas PD recovered sooner for MVC torque and quadriceps EMG. Isotonic performance fatigability differs subtly between PD and healthy controls, providing novel insights into the physical manifestations of fatigue in PD and potential implications for understanding disease progression and management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".