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Record W2914784836 · doi:10.33137/cpoj.v1i2.32008

ASSISTIVE TECHNOLOGIES FOR PAIN MANAGEMENT IN AMPUTEES: A REVIEW

2018· review· en· W2914784836 on OpenAlexvenueaboutno aff
Kamiar Ghoseiri, Mostafa Allami, Mohammad Reza Soroush, Mohammad Yusuf Rastkhadiv

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2018
Typereview
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsAmputationOrthoticsPhantom limb painPhysical medicine and rehabilitationAssistive devicePhysical therapyPhantom limbAssistive technologyMedicinePsychologyPhantom painAffect (linguistics)Computer scienceSurgeryHuman–computer interactionCommunication

Abstract

fetched live from OpenAlex

INTRODUCTION
 The prevalence of limb amputation is increasing globally as a devastating experience that can physically and psychologically affect the lifestyle of a person. The residual limb pain and phantom limb pain are common disabling sequelae after amputation surgery. Assistive devices/technologies can be used to relieve pain in people with amputation. The present review aimed to introduce the existing assistive devices/technologies for pain management in people with amputation.
 Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32008/24427
 How to cite: Ghoseiri K, Allami M, Soroush M.R, Rastkhadiv M.Y. ASSISTIVE TECHNOLOGIES FOR PAIN MANAGEMENT IN AMPUTEES: A REVIEW. 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.v1i2.32008 
 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

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