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Record W1973775110 · doi:10.4061/2010/696492

Dual-Task Interference: The Effects of Verbal Cognitive Tasks on Upright Postural Stability in Parkinson's Disease

2010· article· en· W1973775110 on OpenAlexaff
Jeffrey D. Holmes, Mary E. Jenkins, Andrew M. Johnson, Scott Adams, Sandi J. Spaulding

Bibliographic record

VenueParkinson s Disease · 2010
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsParkinson's diseaseBalance (ability)CognitionPhysical medicine and rehabilitationPsychologyDiseaseTask (project management)AudiologyMemory spanMedicineWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

Although dual-task interference has previously been demonstrated to have a significant effect on postural control among individuals with Parkinson's disease, the impact of speech complexity on postural control has not been demonstrated using quantitative biomechanical measures. The postural stability of twelve participants with idiopathic Parkinson's disease and twelve healthy age-matched controls was evaluated under three conditions: (1) without a secondary task, (2) performing a rote repetition task and (3) generating a monologue. Results suggested a significant effect of cognitive load on biomechanical parameters of postural stability. Although both groups increased their postural excursion, individuals with Parkinson's disease demonstrated significantly reduced excursion as compared with that of healthy age-matched controls. This suggests that participants with Parkinson's disease may be overconstraining their postural adjustments in order to focus attention on the cognitive tasks without losing their balance. Ironically, this overconstraint may place the participant at greater risk for a fall.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations77
Published2010
Admission routes1
Has abstractyes

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