Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".