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Record W4407765011 · doi:10.1371/journal.pone.0318987

Open-source 3D printable forearm crutch

2025· article· en· W4407765011 on OpenAlexaff
Maryam Mottaghi, M. Woods, Laura Danier, Anita So, Jacob M. Reeves, Joshua M. Pearce

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsWestern University
Fundersnot available
KeywordsCrutchOpen source hardwareOpen sourceComputer scienceActivity-based costing3D printingSimulationEngineeringOperating systemMechanical engineeringBusiness

Abstract

fetched live from OpenAlex

Although there has been considerable progress in distributed manufacturing of open-source designs for mobility aids, there is a notable lack of affordable, open-source crutches. Crutches are a vital tool for many individuals with mobility impairments, yet the high costs limit accessibility. Even more, they are in short supply in regions undergoing conflict. The goal of this study is to address this need by leveraging the principles of free and open-source hardware and the capabilities of digital distributed manufacturing to create a low-cost, functional crutch that can be easily produced and customized locally using inexpensive desktop 3D printers. All the design files are open-source, and the design process incorporated load-bearing tests using a hydraulic actuator under static loading conditions to meet the ISO 11334-1:2007 standard for walking aids. The open-source forearm crutch developed in this study not only surpasses the requirements of the ISO method for load capacity (1,516.3 ± 169.9 N, which is 51.6% percent above needs), weighs a fraction of comparable commercial systems (0.612 kg or 27% of proprietary devices), and is customizable, but also offers a highly cost-effective solution; costing CAD $36 in material, which is less than all equivalent crutches on the open market. If recycled plastic is used, the material cost of the crutch could be further reduced to under CAD $13, making it much more accessible.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.254

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 head0.023
GPT teacher head0.224
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

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