MétaCan
Menu
Back to cohort
Record W2121115485 · doi:10.1109/tnsre.2004.838444

Design characteristics of pediatric prosthetic knees

2004· article· en· W2121115485 on OpenAlexaff
Jan Andrysek, Stephen Naumann, William L. Cleghorn

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of TorontoJaneway Children's Health and Rehabilitation Centre
Fundersnot available
KeywordsPhysical medicine and rehabilitationHeelKnee flexionSittingComputer scienceMedicineOrthodonticsPhysical therapyAnatomy

Abstract

fetched live from OpenAlex

We examined whether pediatric prosthetic single-axis knees can theoretically provide the beneficial functional characteristics of polycentric knees and the design considerations needed to realize this. Five children and their parents provided subjective opinions of the relative importance of functional requirements (FRs) for the knee. FRs related to comfort, fatigue, stability, and falling were found to be of high importance, while sitting appearance and adequate knee flexion were of lower importance. Relationships were drawn between these FRs and deductions were made regarding the importance of associated design parameters. Stance-phase control was rated to be of greatest importance followed by toe clearance. Models were developed for five knees including four- and six-bar knees, corresponding to two commercially available components, and for three configurations of a single-axis knee. Stance-phase control, specifically stability after heel-strike and swing-phase initiation at push-off, and toe clearance were simulated. The results suggest that a single-axis knee design incorporating stance-phase control will mutually satisfy the identified set of highly and moderately important FRs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations31
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Neural Systems and Rehabilitation EngineeringSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207