Randomized Controlled Trial of Neural Mobilization After Spinal Surgery
Bibliographic record
Abstract
STUDY DESIGN: Randomized controlled trial with 12-month follow-up. OBJECTIVES: To determine whether the addition of neural mobilization to standard postoperative care improved the outcome of lumbar spinal surgery. SUMMARY OF BACKGROUND DATA: It has been suggested that neural mobilization should be performed after spinal surgery to prevent nerve root adhesions and improve outcome. However, to date, there is no convincing evidence of the value of neural mobilization. METHODS: Eighty-one patients undergoing lumbar discectomy, fusion, or laminectomy at a private hospital in Sydney were randomly allocated to standard postoperative care or standard care plus neural mobilization. Neural mobilization included passive movements and active exercises designed to mobilize the lumbosacral nerve roots and sciatic tract. Primary outcome measures were global perceived effect measured on a 7-point scale, pain measured using visual analogue scales and the McGill Pain Questionnaire, and disability measured with the Quebec Disability Scale. RESULTS: All patients received the treatment as allocated with 12-month follow-up data available for 76 patients (94% of those randomized). There were no statistically significant or clinically significant benefits provided by the neural mobilization treatment for any outcome. CONCLUSIONS: The neural mobilization protocol evaluated in this study did not provide an additional benefit to standard postoperative care for patients undergoing spinal surgery. The authors advocate that this protocol not be used in clinical practice.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".