Development of Surrogate Spinal Cords for the Evaluation of Electrode Arrays Used in Intraspinal Implants
Bibliographic record
Abstract
We report the development of a surrogate spinal cord for evaluating the mechanical suitability of electrode arrays for intraspinal implants. The mechanical and interfacial properties of candidate materials (including silicone elastomers and gelatin hydrogels) for the surrogate cord were tested. The elastic modulus was characterized using dynamic mechanical analysis, and compared with values of actual human spinal cords from the literature. Forces required to indent the surrogate cords to specified depths were measured to obtain values under static conditions. Importantly, to quantify surface properties in addition to mechanical properties normally considered, interfacial frictional forces were measured by pulling a needle out of each cord at a controlled rate. The measured forces were then compared to those obtained from rat spinal cords. Formaldehyde-crosslinked gelatin, 12 wt% in water, was identified as the most suitable material for the construction of surrogate spinal cords. To demonstrate the utility of surrogate spinal cords in evaluating the behavior of various electrode arrays, cords were implanted with two types of intraspinal electrode arrays (one made of individual microwires and another of microwires anchored with a solid base), and cord deformation under elongation was evaluated. The results demonstrate that the surrogate model simulates the mechanical and interfacial properties of the spinal cord, and enables in vitro screening of intraspinal implants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".