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INTEGRATING PROSTHETICS AND MEDICINE

2006· article· en· W2050059736 on OpenAlexaffabout
Peter Kyberd

Bibliographic record

VenueJPO Journal of Prosthetics and Orthotics · 2006
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Fredericton
Fundersnot available
KeywordsRehabilitation engineeringArtificial limbsControl (management)EngineeringWheelchairJoystickRehabilitationPhysical medicine and rehabilitationMedicineProsthesisPhysical therapySimulationComputer scienceArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

The summer of 2005 marked the 40th anniversary of the formation of the Institute of Biomedical Engineering (IBME) by Professor Bob Scott at the University of New Brunswick in Fredericton, Canada. Perhaps in celebration, the summer also saw the largest gathering of delegates to the IBME’s Myoelectric Controls (MEC) Symposium. The IBME was formed after Professor Scott was asked by a physiatrist at a rehabilitation center to help improve a patient’s control of his electric wheelchair. Missing the distal portions of all four limbs, this person’s ability to use the large switched joystick on the chair was awkward and unreliable. The challenges demonstrated by this person’s needs introduced a number of members of the engineering faculty of UNB to the problems of control in the rehabilitation field. Professor Scott initiated a program with research prosthetist Bill Sauter at what was then the Ontario Crippled Children’s Centre that led to investigation of the control issues associated with very short remnant limbs. The research program also ultimately led to the formation of the IBME. The IBME has always focused on the input control aspects of prostheses. An early clinical success was the production of a myoelectric controller that used only one muscle to control two directions of prosthesis motion. It was thus natural for the Institute to promote the use of myoelectric signals as prosthetic controllers through its MEC symposia. MEC gatherings started in the early 1970s as a means to educate the profession about the techniques and practices of myoelectric control. Over the years the symposium has grown, adding workshops and free papers to the mix, and expanding to include any aspect of upper limb provision, not merely myoelectrics. In 2005, the main body of the symposium was free papers, invited speakers, posters and an exhibition of manufacturers’ products. The free papers and presentations were larger than ever, covering prosthetics fitting, patient monitoring, new techniques and devices, and some of the experiences and unique challenges faced by prosthetics teams. The emphasis of the symposium was to relate the practice of prosthetics with medicine. Speakers included some of the most significant workers in the field today. This edition of the Journal of Prosthetics and Orthotics aims to capture the flavor and range of the conference topics. The five-day MEC Symposium underscored the belief that the field is healthy and active and there are many new avenues to be explored in the next few years.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 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

Citations0
Published2006
Admission routes2
Has abstractyes

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