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Record W2976786320

Adapting Isokinetic Dynamometry for Individuals with Transtibial Amputations

2019· article· en· W2976786320 on OpenAlexaff
Oscar Ortiz, Ashirbad Pradhan, Victoria Chester, Usha Kuruganti

Bibliographic record

VenueCMBES Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhysical medicine and rehabilitationProsthesisPhysical therapyMedicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

Transtibial amputations impact one’s ability to perform activities of daily living. Continuous load bearing on the intact limb during ambulation and standing can lead to strength asymmetries in the lower limbs. Objective assessment of strength asymmetries in lower extremity muscles is critical as transtibial amputees are prone to several secondary conditionsstemming from these musculoskeletal imbalances. Isokinetic dynamometry has been used to safely evaluate muscle asymmetries, but testing is usually performed using the participant’sown prosthesis which can vary in available range of motion and suspension method. Furthermore, this methodology excludes those who are not prosthesis users. The purpose of this research was to design, build and test a transtibial adapter for dynamometry that can be used on the residual limb with or without a prosthesis for objective assessment of leg strength. Clinical feedback was sought from one transtibial amputee regarding the usability and comfort of the adapter while performing an isokinetic knee extension/flexion task. The participant was capable of completing the knee contractions without any reported pain or discomfort, suggesting that our prototype may be an option to adapt dynamometry for this population. Further research with the prototype with a larger sample and more contraction conditions is needed to further assess whether the design presented is a viable option to adapt dynamometry for transtibial amputees.

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 score0.263
Threshold uncertainty score0.489

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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