A quality improvement initiative to develop an interprofessional peripheral nerve transfer clinic for individuals with traumatic cervical spinal cord injury
Bibliographic record
Abstract
PURPOSE: Loss of upper extremity (UE) function impacts almost every aspect of daily life and upper limb recovery is reported to be a major priority of individuals living with tetraplegia. Surgical peripheral nerve transfer (PNT) offers the potential to restore volitional control of elbow, wrist and hand function of individuals with C5-C8 tetraplegia AIS A-C. Unfortunately, while there is growing evidence supporting the role of PNT in spinal cord injury (SCI) rehabilitation, there are currently no internationally-recognized consensus-derived best practices for provision of PNT following spinal cord injury (SCI) and few programs have focused on interdisciplinary collaboration during patient selection, surgical decision making, management of medical comorbidities and postoperative rehabilitation. This quality improvement initiative aimed to establish a novel, interdisciplinary PNT program with the goal of optimizing UE recovery and function in individuals with tetraplegia in Canada. MATERIALS AND METHODS: An interprofessional team assembled to complete a detailed exploration of care segments, organizing and sequencing care delivery. RESULTS AND CONCLUSIONS: As a result of this initiative, a care map of planned interprofessional services, their optimal timing across the continuum of care, and clinical functional and community integration outcomes were developed. Data collection and program evaluation are ongoing, and further work to mitigate barriers and develop educational materials around PNT surgery are intended to improve medical decision making and best practice implementation.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".