The BION devices: injectable interfaces with peripheral nerves and muscles
Bibliographic record
Abstract
OBJECT: The purpose of this study was to describe a novel technology for implantable neuromuscular stimulation to treat complications of paralysis and disuse atrophy, including shoulder subluxation, hand contractures, drop foot, and osteoarthritis. The authors review the results so far of several pilot clinical studies of these muscle stimulation devices. METHODS: Miniature wireless stimulators received power and individually addressed command signals from an external radiofrequency transmission coil. One or more implants were injected through a 12-gauge hypodermic insertion tool into muscles or adjacent to motor nerves, where they provided the means to activate the muscles in any desired pattern of intensity and frequency. Randomized controlled studies in small numbers of patients are underway to identify efficacy, acceptability, best methods of practice, and any design changes that may be required to improve the technology. Fifty patients have been enrolled in five studies; 35 patients have undergone implantation of a total of 79 BION1 devices. Comparisons with surface stimulation in patients who have suffered a stroke with shoulder subluxation and hand contractures show similar improvements in objective measures of efficacy but higher comfort levels for stimulation by implants. CONCLUSIONS: Injected microstimulators represent a promising new class of technology for the rehabilitation of patients with upper motor neuropathies. As the technology evolves, practitioners may be able to use it to facilitate functional reanimation of paralyzed limbs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".