Decompression with Brachioradialis Tenotomy Improves Pain and Quality of Life in Patients with Radial Sensory Nerve Compression
Bibliographic record
Abstract
BACKGROUND: Decompression of the superficial sensory branch of the radial nerve (SBRN) with complete brachioradialis tenotomy may treat pain in both simple and complex cases of SBRN compression neuropathy. METHODS: A retrospective chart review was performed of consecutive patients undergoing this procedure between 2008 and 2020 including postoperative outcomes within 90 days. Data were collected and analyzed, including patient and injury demographics, pain descriptors, and patient-reported pain questionnaire, including reported pain severity and impact on quality of life using visual analogue scale (VAS) instruments. Within-group presurgical and postsurgical analyses and between-group statistical analyses were performed. RESULTS: Thirty-three of 58 patients met inclusion criteria. Median time from symptom onset to surgery was 300 days, and median postoperative follow-up time was 37 days. Twenty-five percent of patients ( n = 8) underwent isolated SBRN decompression. The remainder had concomitant decompression of another radial [ n = 16 (48%) or peripheral [ n = 12 (36%)] entrapment point. Ten of 33 patients (30%) had resolution of pain at final follow-up ( P = 0.004). Median change in worst pain over the previous week was -4 ( P < 0.001), and average pain over the last month was -2.75 ( P < 0.001) on the VAS. The impact of pain on quality of life showed a median change of -3 ( P < 0.001) on the VAS. CONCLUSION: Decompression of the sensory branch of the radial nerve including a complete brachioradialis tenotomy improves pain and quality-of-life VAS scores in patients with both simple compression neuropathy syndrome and complex nerve compression syndrome. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".