Efficacy of acupuncture for degenerative lumbar spinal stenosis: protocol for a randomised sham acupuncture-controlled trial
Bibliographic record
Abstract
INTRODUCTION: Degenerative lumbar spinal stenosis (DLSS) is a major public health problem and the primary reason why older adults seek lumbar spine surgery. Acupuncture may be effective for DLSS, but the evidence supporting this possibility is still limited. METHODS AND ANALYSIS: A total of 80 participants with DLSS will be randomly allocated to either an acupuncture group or a sham acupuncture (SA) group at a ratio of 1:1. 24 treatments will be provided over 8 weeks. The primary outcome is the score change of the Modified Roland-Morris Disability Questionnaire (RMDQ) responses from baseline to week 8. The secondary outcomes include the assessment of lower back pain and leg pain using the Numeric Rating Scale (NRS), the change in the number of steps per month, and the assessment of the specific quality of life using the Swiss Spinal Stenosis Questionnaire (SSSQ). We will follow-up with the participants until week 32. All of the participants who received allocation will be included in the statistical analysis. ETHICS/DISSEMINATION: This protocol has been approved by the Research Ethical Committee of Guang'anmen Hospital (Permission number: 2015EC114) and Fengtai Hospital of Integrated Traditional and Western Medicine (Permission number: 16KE0409). The full data set will be made available when this trial is completed and published. Applications for the release of data should be made to ZL (principal investigator). TRIAL REGISTRATION NUMBER: NCT02644746.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.101 | 0.017 |
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".