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Record W1940426470 · doi:10.1155/2015/520128

Balance Dysfunction in Parkinson’s Disease: The Role of Posturography in Developing a Rehabilitation Program

2015· article· en· W1940426470 on OpenAlexaff
Davide Ferrazzoli, Alfonso Fasano, Roberto Maestri, Rossana Bera, Grazia Palamara, Maria Felice Ghilardi, Gianni Pezzoli, Giuseppe Frazzitta

Bibliographic record

VenueParkinson s Disease · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsPosturographyParkinson's diseaseMedicineBalance (ability)RehabilitationPhysical medicine and rehabilitationDiseasePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Balance dysfunction (BD) in Parkinson's disease (PD) is a disabling symptom, difficult to treat and predisposing to falls. The dopaminergic drugs or deep brain stimulation does not always provide significant improvements of BD and rehabilitative approaches have also failed to restore this condition. In this study, we investigated the suitability of quantitative posturographic indicators to early identify patients that could develop disabling BD. Parkinsonian patients not complaining of a subjective BD and controls were tested using a posturographic platform (PP) with open eyes (OE) and performing a simple cognitive task [counting (OEC)]. We found that patients show higher values of total standard deviation (SD) of body sway and along the medio-lateral (ML) axis during OE condition. Furthermore, total and ML SD of body sway during OE condition and total SD of body sway with OEC were higher than controls also in a subgroup of patients with normal Berg Balance Scale. We conclude that BD in Parkinsonian patients can be discovered before its appearance using a PP and that these data may allow developing specific rehabilitative treatment to prevent or delay their onset.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.153
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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.

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

Citations45
Published2015
Admission routes1
Has abstractyes

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