Effect of Neural Mobilization on Nerve-Related Neck and Arm Pain: A Randomized Controlled Trial
Bibliographic record
Abstract
Purpose: Neural mobilization (NM) is often used to treat nerve-related conditions, and its use is reasonable with nerve-related neck and arm pain (NNAP). The aims of this study were to establish the effect of NM on the pain, function, and quality of life (QOL) of patients with NNAP and to establish whether high catastrophizing and neuropathic pain influence treatment outcomes. Method: A randomized controlled trial compared a usual-care (UC; n = 26) group, who received cervical and thoracic mobilization, exercises, and advice, with an intervention (UCNM; n = 60) group, who received the same treatment but with the addition of NM. Soft tissue mobilization along the tract of the nerve was used as the NM technique. The primary outcomes were pain intensity (rated on the Numerical Pain Rating Scale), function (Patient-Specific Functional Scale), and QOL (EuroQol-5D) at 3 weeks, 6 weeks, 6 months, and 12 months. The secondary outcomes were the presence of neuropathic pain (using the Neuropathic Diagnostic Questionnaire) and catastrophizing (Pain Catastrophising Scale). Results: Both groups improved in terms of pain, function, and QOL over the 12-month period ( p < 0.05). No between-groups differences were found at 12 months, but the UCNM group had significantly less pain at 6 months ( p = 0.03). Patients who still presented with neuropathic pain ( p < 0.001) and high pain catastrophizing ( p = 0.02) at 6- and 12-mo follow-ups had more pain. Conclusions: Both groups had similar improvements in function and QOL at 12-month follow-up. The UCNM group had significantly less pain at 6-month follow-up and a lower mean pain rating at 12-month follow-up, although the difference between groups was not significant. Neuropathic pain is common among this population and, where it persisted, patients had more pain and functional limitations at 12-mo follow-up.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".