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Record W4220886977 · doi:10.3138/jmvfh-2021-0094

Microprocessor knee versus non-microprocessor knee for backup device in lower limb prostheses: A qualitative study

2022· article· en· W4220886977 on OpenAlexaffvenueabout
Jody-Lynn Young, Eva Guérin, Markus Besemann, Nancy Dudek

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2022
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsCanadian Armed ForcesDepartment of National DefenceUniversity of Ottawa
Fundersnot available
KeywordsBackupProsthesisAmputationMicroprocessorKnee prosthesisUnit (ring theory)Physical medicine and rehabilitationMedicineComputer scienceSurgeryPsychologyEmbedded systemOperating system

Abstract

fetched live from OpenAlex

LAY SUMMARY Current policy in the Canadian Armed Forces (CAF) and Veterans Affairs Canada (VAC) is to provide individuals with an amputation through or above the knee with a prosthesis with a microprocessor knee (MPK) unit for daily use and a backup prosthesis with a non-microprocessor knee (N-MPK) unit. These knee units have significant functional differences. The purpose of this study was to gain an understanding of users’ device preference and the impact of switching between the MPK and N-MPK. To study this, six members participated in semi-structured interviews. Analysis found seven major categories driving prosthetic preference: functionality, physical aspects, mental aspects, activity, maintenance, safety, and health-related quality of life. The MPK was superior in all categories, and immediate switching between devices was problematic. As a result, participants who had an N-MPK backup did not use the device and instead received a loaner MPK from their prosthetist when required. These results suggest that for individuals who do not have ready access to their prosthetist to obtain a loaner knee unit, consideration should be given for a backup prosthesis with the same MPK unit as their daily-use prosthesis. Otherwise, no routine need for a backup N-MPK was identified.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.323
Teacher spread0.293 · 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 designQualitative
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

Citations3
Published2022
Admission routes3
Has abstractyes

Explore more

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