An Examination of Utilization Rates Over Time of Nerve and Tendon Transfers in Canada to Improve Upper Limb Function in Cervical Spinal Cord Injury
Bibliographic record
Abstract
Introduction: Upper limb function loss in cervical spinal cord injury (SCI) contributes to substantial disability, and negatively impacts quality of life. Nerve transfer and tendon transfer surgery can provide improved upper limb function. This study assessed the utilization of nerve and tendon transfer surgery for individuals with tetraplegia in Canada. Methods: Data from the Canadian Institute for Health Information's Discharge Abstracts Database and the National Ambulatory Care Reporting System were used to identify the nerve and tendon transfer procedures performed in individuals with tetraplegia (2004-2020). Cases were identified using cervical SCI ICD-10-CA codes and Canadian Classification of Intervention codes for upper extremity nerve and tendon transfers. Data on sex, age at time of procedure, province, and hospital stay duration were recorded. Results: From 2004 to 2020, there were ≤80 nerve transfer procedures (81% male, mean age 38.3 years) and 61 tendon transfer procedures (78% male, mean age 45.0 years) performed (highest in Ontario and British Columbia). Using an estimate of 50% eligibility, an average of 1.3% of individuals underwent nerve transfer and 1.0% underwent tendon transfer. Nerve transfers increased over time (2004-2009, n = <5; 2010-2015, n = 27; 2016-2019, n = 49) and tendon transfers remained relatively constant. Both transfer types were performed as day-surgery or single night stay. Conclusions: Nerve and tendon transfer surgery to improve upper limb function in Canadians with tetraplegia remains low. This study highlights a substantial gap in care for this vulnerable population. Identification of barriers that prevent access to care is required to promote best practice for upper extremity care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".