MétaCan
Menu
Back to cohort
Record W2125995847 · doi:10.1109/tnsre.2005.856071

Design and quantitative evaluation of a stance-phase controlled prosthetic knee joint for children

2005· article· en· W2125995847 on OpenAlexaff
Jan Andrysek, Stephen Naumann, William L. Cleghorn

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsStairsKnee JointPhysical medicine and rehabilitationMedicinePhysical therapyKnee flexionJoint (building)EngineeringSurgeryStructural engineering

Abstract

fetched live from OpenAlex

The aims of this study were to demonstrate a theoretical basis for the design of a stance-phase controlled paediatric prosthetic knee joint, clinically test prototypes of the knee, and use a questionnaire to evaluate its efficacy. Biomechanical models were used to analyze the stance-phase control characteristics of the proposed knee, and those of three other commonly prescribed paediatric knee joint mechanisms, which were also the conventional knee joints used by the six participants of this study (mean age 10.8 years). A questionnaire pertaining to stance-phase control was designed and administered twice to each child; once for the evaluation of the prototype knee joint and once for the conventional knee joint. Stance-phase modeling results indicated decreased zones of instability for the new knee as compared to other paediatric knee joints. Questionnaire results revealed a decrease in the frequency of falls with the prototype compared to other knees, especially in highly active children. The children also reported worrying less about the knee collapsing during walking. No differences were evident for stance-phase stability during running, walking on uneven terrain, ambulating up and down stairs and inclines, fatigue, and types of activities performed.

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)0.001
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: none
Teacher disagreement score0.630
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.265
Teacher spread0.236 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Neural Systems and Rehabilitation EngineeringSame topicLower Extremity Biomechanics and PathologiesFrench-language works237,207