Development of an instrumented spinal cord surrogate using optical fibers: A feasibility study
Bibliographic record
Abstract
In vitro replication of traumatic spinal cord injury is necessary to understand its biomechanics and to improve animal models. During a traumatic spinal cord injury, the spinal cord withstands an impaction at high velocity. In order to fully assess the impaction, the use of spinal canal occlusion sensor is necessary. A physical spinal cord surrogate is also often used to simulate the presence of the spinal cord and its surrounding structures. In this study, an instrumented physical spinal cord surrogate is presented and validated. The sensing is based on light transmission loss observed in embedded bare optical fibers subjected to bending. The instrumented surrogate exhibits similar mechanical properties under static compression compared to fresh porcine spinal cords. The instrumented surrogate has a compression sensing threshold of 40% that matches the smallest compression values leading to neurological injuries. The signal obtained from the sensor allows calculating the compression of the spinal cord surrogate with a maximum of 5% deviation. Excellent repeatability was also observed under repetitive loading. The proposed instrumented spinal cord surrogate is promising with satisfying mechanical properties and good sensing capability. It is the first attempt at proposing a method to assess the internal loads sustained by the spinal cord during a traumatic injury.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".