Characterization of Spinal Cord Injury Patients for Arm Functional Restoration through Nerve Transfer
Bibliographic record
Abstract
INTRODUCTION: Traumatic spinal cord injuries (tSCI) are common, often leaving patients irreparably debilitated. Therefore, novel strategies such as nerve transfers (NT) are needed for mitigating secondary SCI damage and improving function. Although different tSCI NT options exist, little is known about the epidemiological and injury-related aspects of this patient population. Here, we report such characteristics to better identify and understand the number and types of tSCI individuals who may benefit from NTs. MATERIALS AND METHODS: Two peripheral nerve experts independently evaluated all adult tSCI individuals < 80 years old admitted with cervical tSCI (C1-T1) between 2005 and 2019 with documented tSCI severity using the ASIA Impairment Scale for suitability for NT (nerve donor with MRC strength ≥ 4/5 and recipient ≤ 2/5). Demographic, traumatic injury, and neurological injury variables were collected and analyzed. RESULTS: A total of 709 tSCI individuals were identified with 224 (32%) who met the selection criteria for participation based on their tSCI level (C1-T1). Of these, 108 (15% of all tSCIs and 48% of all cervical tSCIs) were deemed to be appropriate NT candidates. Due to recovery, 6 NT candidates initially deem appropriate no longer qualified by their last follow-up. Conversely, 19 individuals not initially considered appropriate then become eligible by their last follow-up. CONCLUSION: We found that a large proportion of individuals with cervical tSCI could potentially benefit from NTs. To our knowledge, this is the first study to detail the number of tSCI individuals that may qualify for NT from a large prospective database.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".