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Record W4387893625 · doi:10.1080/00207454.2023.2273765

Static posturography analysis for postural instability in patients with Parkinson’s disease

2023· article· en· W4387893625 on OpenAlexaff
Silvia Aparecida Ferreira Peruzzo, Carlos Henrique Ferreira Camargo, Marise Bueno Zonta, Ramina Pessoa Renata, Renato P. Munhoz, Hélio Afonso Ghizoni Teive

Bibliographic record

VenueInternational Journal of Neuroscience · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease and Spinal Disorders
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsPosturographyPostural instabilityParkinson's diseasePhysical medicine and rehabilitationPsychologyMedicineDiseaseBalance (ability)Physical therapyInternal medicine

Abstract

fetched live from OpenAlex

Background Parkinson’s disease (PD) is initially characterized by a rigid-akinetic syndrome and later by the development of postural instability. This condition often leads to balance impairments, potentially resulting in postural deformities and foot asymmetry. This study aimed to assess balance changes in PD patients.Methods We evaluated 56 PD patients and 30 controls using static posturography. The variables examined included foot type, the primary region of body weight distribution on the plantar surface, statokinesigram (center of pressure [COP]), and stabilogram (COP in the X- and Y-axes).Results PD patients exhibited a high prevalence of pes cavus (92.8%) (p < 0.001) and greater weight distribution toward the hindfoot. In all assessments, COP sway areas were significantly larger in PD patients (p < 0.001). The total COP area increased in the non-fall group during the foot parallel assessment and in the X-axis in the fall group with eyes open (p = 0.046) and closed (p = 0.023). Significant correlations between body weight discharge on the plantar area and COP sway were observed, particularly in the non-postural instability group in the COP X-axis with feet parallel and eyes open on the more rigid limb and in the postural instability group in the Y-axis with feet parallel and eyes open on the more rigid limb and with eyes closed bilaterally.Conclusion Progressive rigidity may disrupt compensatory mechanisms, altering foot morphology, shifting body weight discharge posteriorly, and increasing COP sway. This cascade of events results in impaired balance and an elevated risk of falls.

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 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 score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.308
Teacher spread0.288 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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