Anterior Interosseus to Ulnar Motor Nerve Transfers: A Canadian Perspective
Bibliographic record
Abstract
Background: The anterior interosseus nerve (AIN) to ulnar motor nerve transfer has been popularized as an adjunct to surgical decompression in patients with severe cubital tunnel syndrome (CuTS) and high ulnar nerve injuries. The factors influencing its implementation in Canada have yet to be described. Methods: An electronic survey was distributed to all members of the Canadian Society of Plastic Surgery (CSPS) using REDCap software. The survey examined 4 themes: previous training/experience, practice volume of nerve pathologies, experience with nerve transfers, and approach to the treatment of CuTS and high ulnar nerve injuries. Results: A total of 49 responses were collected (12% response rate). Of all, 62% of surgeons would use an AIN to ulnar motor supercharge end-to-side (SETS) transfer for a high ulnar nerve injury. For patients with CuTS and signs of intrinsic atrophy, 75% of surgeons would add an AIN-SETS transfer to a cubital tunnel decompression. Sixty-five percent would also release Guyon’s canal, and the majority (56%) use a perineurial window for their end-to-side repair. Eighteen percent of surgeons did not believe the transfer would improve outcomes, 3% cited lack of training, and 3% would preferentially use tendon transfers. Surgeons with hand fellowship training and those less than 30 years in practice were more likely to use nerve transfers in the treatment of CuTS ( P < .05). Conclusions: Most CSPS members would use an AIN-SETS transfer in the treatment of both a high ulnar nerve injury and severe CuTS with intrinsic atrophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".