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Record W3019185976 · doi:10.1123/jab.2019-0365

Standing Balance Performance and Knee Extensors’ Strength in Diabetic Patients with Neuropathy

2020· article· en· W3019185976 on OpenAlexaff
Hananeh Younesian, Nader Farahpour, Mehrdokht Mazde, Martin Simoneau, Katia Turcot

Bibliographic record

VenueJournal of Applied Biomechanics · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsMedicineBalance (ability)Nerve conduction velocityPeripheral neuropathyDiabetes mellitusPhysical medicine and rehabilitationCenter of pressure (fluid mechanics)Diabetic neuropathyElectromyographyPhysical therapyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetes peripheral neuropathy (DPN) leads to balance impairment among diabetes mellitus (DM). The aims of this study were to (1) distinguish between DM patients who have/do not have DPN and to (2) compare quadriceps' strength and balance performance of DM, DPN, and healthy groups. Fifteen healthy females and 33 females with type 2 diabetic patients participated. The electrodiagnostic method was used to classify diabetic patients into DM and DPN. A dynamometer was used to measure quadriceps' strength. Single-leg standing on a force plate was also used to quantify participants' balance. Smaller conduction velocity and amplitude and greater distal latency of all nerves were observed in the DPN compared with the DM in particular for sensory nerve. In DPN, conduction velocity was asymmetrical. The quadriceps' strength of both legs in DPN and the right leg in DM was smaller than in the control group. The root mean square of the center of pressure was similar between DM and DPN. But it was larger in DPN than in the control group. DPN is associated with asymmetrical conduction velocity, smaller quadriceps' strength, and weaker balance performance that is suggestive of higher risk of falling. Balance training is recommended for the DPN group during their rehabilitation to reduce their falling risk.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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