Nerve transfers for treatment of isolated axillary nerve injuries
Bibliographic record
Abstract
BACKGROUND: The most common neurological defect in traumatic anterior glenohumeral dislocation is isolated axillary nerve palsy. Most recover spontaneously; however, some have persistent axillary neuropathy. An intact rotator cuff may compensate for an isolated axillary nerve injury; however, given the high rate of rotator cuff pathology with advancing age, patients with an axillary nerve injury are at risk for complete shoulder disability. OBJECTIVE: To review reconstruction of the axillary nerve to alleviate shoulder pain, augment shoulder stability, abduction and external rotation to alleviate sole reliance on the rotator cuff to move and stabilize the shoulder. METHODS: A retrospective review of 10 patients with an isolated axillary nerve injury and an intact rotator cuff who underwent a triceps nerve branch to axillary nerve transfer was performed. Patient demographics, surgical technique, deltoid strength, donor-site morbidity, complications and time to surgery were evaluated. RESULTS: Ten male patients, mean age 38.3 years (range 18 to 66 years), underwent a triceps to axillary nerve transfer for isolated axillary nerve injury 7.4 months (range five to 12 months) post-traumatic shoulder dislocation. Deltoid function was British Medical Research Council grade 0/5 in all patients preoperatively and ≥3/5 deltoid strength in eight patients at final follow-up (14.8 months [range 12 to 25 months]). There were no complications and no donor-site morbidity. CONCLUSION: A triceps to axillary nerve transfer for isolated axillary neuropathy following traumatic shoulder dislocation improved shoulder pain, stability and deltoid strength, and potentially preserves shoulder function with advancing age by alleviating sole reliance on the rotator cuff for shoulder abduction and external rotation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".