MétaCan
Menu
Back to cohort
Record W2912987644 · doi:10.33137/cpoj.v1i2.32012

BIOMECHANICAL ANALYSIS OF DIFFERENT PROSTHETIC TECHNOLOGIES FOR TRANS-FEMORAL AMPUTEES DURING SLOPE DESCENT

2018· article· en· W2912987644 on OpenAlexvenueaboutno aff
Nadine Stech, Michael McGrath, Piotr Laszczak, Alan Kercher, David Moser

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical medicine and rehabilitationBiomechanicsAnkleCrutchDescent (aeronautics)GaitMedicinePhysical therapyPsychologyComputer scienceEngineeringSurgeryAnatomy

Abstract

fetched live from OpenAlex

INTRODUCTION
 Lower limb amputees have different biomechanics to able-bodied people when walking on slopes1,2, often struggling to negotiate different gradients safely. Loss of proprioception and muscular control contributes to this issue, which is a particular problem for trans-femoral amputees, where both ankle and knee joints are absent. Studies have shown that prosthetic technologies can have benefits for slope negotiation. The aim of this study was to isolate the specific effects of different trans-femoral prosthetic technologies, by applying each additional mechanism incrementally.
 Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32012/24431
 How to cite: Stech N, McGrath M, Laszczak P, Kercher A, Zahedi S, Moser D. BIOMECHANICAL ANALYSIS OF DIFFERENT PROSTHETIC TECHNOLOGIES FOR TRANS-FEMORAL AMPUTEES DURING SLOPE DESCENT. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, POSTER PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32012 
 Abstracts were Peer-reviewed by the American Orthotic Prosthetic Association (AOPA) 101st National Assembly Scientific Committee. 
 http://www.aopanet.org/

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.211 · 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
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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Prosthetics & Orthotics JournalSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207