Radial Nerve Mobilization Reduces Lateral Elbow Pain and Provides Short-Term Relief in Computer Users§
Bibliographic record
Abstract
STUDY DESIGN: Prospective Experimental Study. BACKGROUND: Computer users may be at risk of lateral elbow pain. It is theorized that adverse mechanical tension can arise in the radial nerve with sustained keyboarding due to sustained static work of the elbow extensor muscles. Neural mobilization has been suggested as a potential treatment. PURPOSE: The purpose of this study was to evaluate the effect of neural mobilization of the radial nerve on a single occasion in terms of its ability to reduce lateral elbow pain. METHODS AND ANALYSIS: Forty-one computer professionals (Mean age 46.7; S.D. 12.77), who had experienced lateral elbow pain for a mean of 2.87 months were recruited. The participants rated the pain using a verbal, numeric rating scale (NRS). Radial nerve tension was tested using the Upper limb Tension Test (ULTT) for radial nerve in both upper extremities. The radial nerve was mobilized using a series of 8 oscillations and repeated 3 times with a one minute rest in between. The NRS and ULLT were repeated after treatment and the scores compared using a paired t-test by the first author. RESULTS: The mean NRS scores decreased significantly from 5.7 (1.1) to 3.8 (1.4) (p<0.000; t value=8.07). CONCLUSION: A single session of 3 neural mobilization resulted in a reduction of pain in computer users with lateral elbow pain. A long-term randomized trial is needed to determine the effects sustained over-time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".