Repetitive transcranial magnetic stimulation alone and in combination with motor control exercise for the treatment of individuals with chronic non-specific low back pain (ExTraStim trial): study protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: While multiple pharmacological and non-pharmacological interventions treating chronic non-specific low back pain (CLBP) are available, they have been shown to produce at best modest effects. Interventions such as repetitive transcranial magnetic stimulation (rTMS), a form of non-invasive brain stimulation, have exhibited promising results to alleviate chronic pain. However, evidence on the effectiveness of rTMS for CLBP is scarce due to limited rigorous clinical trials. Combining rTMS with motor control exercises (MCE) may help to address both central and nociceptive factors contributing to the persistence of LBP. The primary aim of this randomised controlled trial is to compare the effectiveness of a combination of rTMS and MCE to repeated rTMS sessions alone, sham rTMS and a combination of sham rTMS and MCE on pain intensity. METHODS AND ANALYSIS: One hundred and forty participants (35/group) with CLBP will be randomised into four groups (active rTMS+MCE, sham rTMS+MCE, active rTMS and sham rTMS) to receive 10 sessions of their allocated intervention. The primary outcome will be the pain intensity, assessed at baseline, 4, 8, 12 and 24 weeks. Secondary outcomes will include disability, fear of movement, quality of life and patient global rating of change. ETHICS AND DISSEMINATION: in June 2019 (#2020-1844 - CER CIUSSS-CN). The results of the study will be submitted to a peer-reviewed journal and scientific meetings. TRIAL REGISTRATION NUMBER: NCT04555278.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.015 |
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".