Poster (Knowledge Generation) ID 2002621
Bibliographic record
Abstract
Background Nerve transfer surgery for patients with spinal cord injury (SCI) is an emergent practice for upper extremity reconstruction currently being implemented in a few clinical settings around the world. In 2019, we implemented an interdisciplinary clinic for upper extremity care in tetraplegic patients. Since the beginning of this collaboration, thirteen patients underwent nerve transfer surgery and followed a specific two-year rehabilitation protocol. Presentation objectives: To present pre-surgery to post-rehabilitation data To review the rehabilitation protocol To reflect on future implications and improvements of the clinical model To discuss possible collaborations regarding this approach throughout Canada Methods Thirteen patients underwent a surgical reconstruction of their upper extremity. They followed a rehabilitation process with occupational therapists and physiotherapists. So far, eight patients have completed their two-year rehabilitation process. Routine outcome assessments were collected at 0, 6, 12, 24 months following the surgery. Results Twenty-four arms were operated, for a total of 51 nerve transfers. We will present our epidemiological data, details of the surgeries and results on hand opening, grip and pinch strength, functional testing, as well as satisfaction questionnaires. Conclusion Nerve transfer surgery is an exciting and innovative technique for our SCI patients. An interdisciplinary setting is the key to success. Our data shows that this surgery, well planned and accompanied by proper rehabilitation, is a promising avenue to improve upper extremity function in people with cervical spinal cord injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.898 | 0.679 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".