Surgical Strategies for Functional Upper Extremity Reconstruction After Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injuries (SCI) can substantially affect independence and quality of life, particularly by limiting upper extremity function. Surgical reconstruction offers the potential to restore motion in the hand, wrist, and elbow for those with deficits following cervical spinal cord injury. Techniques such as tendon transfer, tenodesis, and arthrodesis-often used in combination-are well-established strategies for enhancing upper extremity function. Nerve transfers have more recently been employed and differ from other procedures in that they are often time sensitive and should be performed before permanent muscle atrophy occurs. A comprehensive preoperative evaluation, including clinical examination and electrodiagnostic assessment, is essential to determine the availability and strength of donor tendons and nerves. The International Classification of Surgery for the Hand in Tetraplegia (ICSHT) system is the most utilized surgical classification for determining muscle that can be used for reconstruction. Based on this classification, prioritization is given to restoring elbow extension, wrist extension, pinch, and grasp. Postoperative rehabilitative therapy balances the need for immobilization while preventing joint stiffness and may also incorporate cortical retraining strategies to activate tendon and nerve transfers. Ultimately, a collaborative, interdisciplinary approach is essential for assessing the injury, determining operative candidacy, selecting the optimal treatment strategy, and providing tailored rehabilitation. This article explores the classification of SCI as it pertains to the upper limb, provides an overview of surgical options, describes the preoperative clinical and electrodiagnostic evaluation process, and discusses reconstructive strategies aimed at improving functional outcomes in individuals with SCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".