Serial estimation of motor unit numbers using an implantable system following nerve injury and repair in rats
Bibliographic record
Abstract
Motor unit number estimation (MUNE) is an established technique to assess recovery following peripheral nerve injury. In rats, where the vast majority of peripheral nerve research is conducted, assessing motor units at various time points requires a terminal procedure due to the invasive nature of current techniques. Here, we present an implanted system that was used to serially assess MUNE after peripheral nerve injury and repair in rats. This system significantly increases the efficiency of peripheral nerve research by negating the need for terminal procedures, allowing for serial MUNE assessment over time in the same rat. Our system utilizes a commercial implantable stimulator, custom designed cuff electrode, and corresponding custom software with automatic M-wave classification to quickly assess functional reinnervation up to 8 weeks following nerve injury and repair. The concepts presented in this paper are applicable to any implanted device with a transcutaneous radio frequency or inductive link that can be used to trigger nerve stimulation. The methodology is also applicable to researchers without access to implantable devices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".