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Record W2911495424 · doi:10.33137/cpoj.v1i2.32042

PROSTHETIC LIMB USER EXPERIENCES WITH CROSSOVER FEET: A FOCUS GROUP STUDY TO EXPLORE OUTCOMES THAT MATTER

2018· article· en· W2911495424 on OpenAlexvenueaboutno aff
Cody L. McDonald, Sarah M. Cheever, Sara J. Morgan, Brian J. Hafner

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsAmputationFocus groupProsthesisPsychologyPresentation (obstetrics)MedicinePhysical therapyPhysical medicine and rehabilitationSurgerySociology

Abstract

fetched live from OpenAlex

INTRODUCTION
 A variety of prosthetic feet are available to meet the diverse needs of people with lower limb amputation. Outcome measures selected to assess comparative effectiveness of prosthetic feet are most often chosen by clinicians and researchers.1 Therefore, these measures may not reflect the outcomes that are most important to lower limb prosthesis users. Qualitative research can give voice to prosthesis users and promote the consideration of user priorities when selecting outcome measures for clinical assessment and research studies. This study explored the lived experience and outcomes of importance to individuals who have worn both traditional energy storing feet and crossover feet.
 Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32042/24456
 How to cite: McDonald C.L, Cheever S.M , Morgan S.J., Hafner B.J. PROSTHETIC LIMB USER EXPERIENCES WITH CROSSOVER FEET: A FOCUS GROUP STUDY TO EXPLORE OUTCOMES THAT MATTER. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, ORAL PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32042 
 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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 designObservational
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

Citations1
Published2018
Admission routes2
Has abstractyes

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