Median Nerve Compression in the Forearm: A Clinical Diagnosis
Bibliographic record
Abstract
Background: Median nerve entrapment in the forearm (MNEF) without motor paralysis is a challenging diagnosis. This retrospective study evaluated the clinical presentation, diagnostic studies, and outcomes following surgical decompression of MNEF. Methods: The study reviewed 147 patient medical charts following MNEF surgical decompression. With exclusion of patients with combined nerve entrapments (radial and ulnar), polyneuropathy, neurotmetic nerve injury, or median nerve motor palsy, the study sample included 27 patients. Data collected include: clinical presentation and pain, strength, provocative testing, functional outcomes, and Disabilities of the Arm, Shoulder and Hand (DASH) scores. Results: The study included 27 patients (mean follow-up = 7 months), and 13 patients had previous carpal tunnel release (CTR). Clinical presentation included pain (n = 27) (forearm, n = 22; median nerve innervated digits, n = 21; and palm, n = 21) and positive clinical tests (forearm scratch collapse test, n = 27; pain with compression over the flexor digitorum superficialis arch/pronator, n = 24; Tinel sign, n = 11). Positive electrodiagnostic studies were found for MNEF (n = 2) and carpal tunnel syndrome (n = 11). Primary CTR was performed in 10 patients and revision CTR in 7 patients. Postoperatively, there were significant ( P < .05) improvements in strength, pain, quality of life, and DASH scores. Conclusions: The MNEF without motor paralysis is a clinical diagnosis supported by pain drawings, pain quality, and provocative tests. Patients with persistent forearm pain and median nerve symptoms (especially after CTR) should be evaluated for MNEF. Surgical decompression provides satisfactory outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".