Targeted Muscle Reinnervation Using the Anterior Interosseous Nerve for Symptomatic Wrist Level Neuromas
Bibliographic record
Abstract
BACKGROUND: Iatrogenic and traumatic sensory nerve injury at the level of the wrist can lead to debilitating neuroma. Targeted muscle reinnervation (TMR) is an effective treatment for the management of symptomatic neuromas. We investigate the use of the anterior interosseous nerve (AIN) as a recipient nerve for the treatment of iatrogenic neuromas. This case series describes 4 patients treated for neuromas of the lateral antebrachial cutaneous nerve (LABC), palmar cutaneous branch of median nerve (PCB), and radial sensory nerve (RSN). METHODS: Four cases involved a symptomatic neuroma of the LABC, PCB, or RSN. These were treated with TMR, using the AIN motor branch to pronator quadratus. The neuromas were identified in all 4 cases and transected distally. The AIN was identified through a proximal extension of the exploratory incision and an end-to-end coaptation was performed to the proximal aspect of the donor nerve and the distal AIN. RESULTS: All 4 patients underwent routine follow-up for a duration of 2 to 10 months, with a long-term follow-up from 25 to 49 months. At routine follow-up, all patients reported resolution of pain and symptoms and had a negative Tinel's sign over their previous neuroma site. At the long-term follow-up, 2 patients reported recurrence of hyperesthesia, both to a lesser severity than before treatment. All 4 patients reported returning to work or routine and stated the TMR procedure improved their pain and symptoms. CONCLUSIONS: The motor branch of the distal AIN to pronator quadratus is a viable option as a TMR recipient for the management of symptomatic neuromas of the wrist level. Long-term follow-up shows reduction of reported pain and improvement of function.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".