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Record W2030939594 · doi:10.1179/016164102101200311

Neuroprostheses for grasping

2002· review· en· W2030939594 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurological Research · 2002
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsNeuroprostheticsFunctional electrical stimulationPhysical medicine and rehabilitationComputer sciencePopulationNeuroscienceMedicinePsychologyStimulation

Abstract

fetched live from OpenAlex

In recent years a number of neuroprostheses have been developed and used to assist stroke and spinal cord injured subjects to restore or improve grasping function. These neuroprostheses clearly demonstrated that the targeted group of subjects can significantly benefit from this technology and that functional electrical stimulation (FES) is a viable method for restoring or improving grasping function. In this article the FES technology is briefly explained and some of the better known neuroprostheses for grasping are discussed. Furthermore, a typical population of subjects that can benefit from this technology is indicated as well as the methodology to select and train these subjects to apply the neuroprosthesis in daily living activities. This article also provides a brief summary of the achieved results with the existing neuroprostheses for grasping and discusses some of the challenges this technology is currently facing.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.364
GPT teacher head0.431
Teacher spread0.067 · 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