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Record W4400170232 · doi:10.33137/cpoj.v7i1.43034

A NEW TEMPORARY TRAINING PROSTHESIS FOR PEOPLE WITH TRANSTIBIAL AMPUTATION: A TECHNICAL NOTE

2024· article· en· W4400170232 on OpenAlexaffvenueabout
Hossein Gholizadeh, Natalie Baddour, Nancy Dudek, Edward D. Lemaire

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsOttawa HospitalMining Association of CanadaUniversity of Ottawa
Fundersnot available
KeywordsProsthesisAmputationThighPhysical medicine and rehabilitationMedicinePhysical therapyOrthodonticsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: While waiting to receive a prosthesis, individuals with amputations could benefit from using a temporary training prosthesis to expedite the rehabilitation process and prepare them for subsequent walking with their prosthesis. OBJECTIVES: To design and build a temporary training prosthesis for people with a transtibial amputation. METHODOLOGY: Various temporary training prostheses were designed and simulated using SolidWorks software, followed by fabricating and testing multiple prototypes. Initial tests were conducted on five able bodied subjects without amputation to evaluate comfort, ensure the prototype functioned as intended, and to refine the design. The final prototype design had no weight-bearing on the residual limb end and required the person to wear a shrinker or silicone liner. FINDINGS: SolidWorks simulations showed that the device could tolerate up to 200 kg load. Subjective feedback indicated that body weight is primarily supported by the thigh section, while partially utilizing the patellar tendon and tibial flares. The thigh section can be shifted 5 cm up or down and 2.5 cm to the front or back from the knee joint center (to enhance knee stability or function). Additionally, the thigh angle can be adjusted to 0, 5, 10, or 15 degrees to accommodate hip flexion contracture. The shank section width is adjustable and can be shifted up or down based on the residual limb shape. All five able-bodied participants successfully walked with the non-amputee version of the temporary prosthesis prototype and the device withstood walking, sitting, and standing loads. CONCLUSION: An adjustable temporary training prosthesis was successfully designed, and pilot tested by five able-bodied individuals. Future testing will involve five experienced prosthetic users before conducting trials with individuals with a new transtibial amputation. Layman's Abstract A novel temporary training prosthesis was developed for individuals with new transtibial amputations while they await the healing of their residual limb and the receipt of a prosthesis. Utilizing SolidWorks computer-aided design software, various designs were explored, and rigorous testing was conducted to ensure durability and functionality. Adjustable features were incorporated to accommodate different body shapes and sizes. Initial testing on five people without amputations confirmed device effectiveness in supporting walking, sitting, and standing. Next, performance will be assessed on five experienced prosthetic users before proceeding to trials with individuals with a new transtibial amputation. This temporary prosthesis aims to expedite rehabilitation, enabling people to regain mobility and independence more efficiently. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43034/32865 How To Cite: Gholizadeh H, Baddour N, Dudek N, Lemaire E.D. A new temporary training prosthesis for people with transtibial amputation: A technical note. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 1, No.2. https://doi.org/10.33137/cpoj.v7i1.43034 Corresponding Author: Hossein Gholizadeh, PhD, CP (Canada)Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada.E-Mails: hgholizadeh@uottawa.ca, gholizadeh87@yahoo.comORCID ID: https://orcid.org/0000-0001-5847-7985

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2024
Admission routes3
Has abstractyes

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