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Record W1967703150 · doi:10.2217/nmt.11.49

Comparison of Exercise Strategies for Motor Symptom Improvement in Parkinson’s Disease

2011· article· en· W1967703150 on OpenAlexafffund
Michael Sage, Rose Ellen Johnston, Quincy J. Almeida

Bibliographic record

VenueNeurodegenerative Disease Management · 2011
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsWilfrid Laurier University
FundersParkinson CanadaParkinson Society Canada
KeywordsPhysical therapyPhysical medicine and rehabilitationParkinson's diseaseAerobic exerciseRating scaleMedicineRandomized controlled trialModalitiesPsychologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

SUMMARY Aims: To evaluate the effectiveness of four exercise interventions on motor symptoms of Parkinson’s disease (PD). Materials and methods: This was a quasi-experimental trial with 89 participants assigned to one of four exercise programs (aquatic, aerobic, strength and sensory attention-focused exercise) or a control group. All groups were assessed by a blinded evaluator with the Unified Parkinson’s Disease Rating Scale (UPDRS III) motor section before exercises began (pre-test), immediately following exercise (post-test) and a subgroup was followed for a 6-week nonexercise washout period (washout). Results: Only sensory attention-focused exercise resulted in significant symptomatic improvement relative to nonexercising control participants. The sensory (6.7 points) and strength training (5.5 points) groups also had significant UPDRS III reductions from pre- to post-exercise. These benefits were not maintained after the washout period. Conclusion: Of the exercise modalities tested, sensory attention-focused exercise and strength training were the most effective strategies for individuals with PD. Future randomized trials are needed to confirm these results and compare other promising strategies aimed at specific pathophysiological deficits of PD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.303
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2011
Admission routes2
Has abstractyes

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