The Effect of Noninvasive Brain Stimulation to Reduce Nonspecific Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: We conducted a systematic review/meta-analysis to evaluate noninvasive brain stimulation (NIBS) efficacy to alleviate pain and improve disability in low back pain (LBP). MATERIALS AND METHODS: A systematic literature search was performed by a librarian in MEDLINE, Embase, EBM Reviews, CINAHL, and Web of Science databases (last search: January 14, 2021). Data were pooled by the number of sessions and follow-up periods. Independent reviewers performed screening, data extraction, and risk of bias. Pain reduction and disability were used as outcomes. RESULTS: Twelve articles were included in the qualitative synthesis and 8 in the meta-analysis. A single session of NIBS reduced pain compared with sham (standardized mean difference: -0.47; P<0.001; very low-quality evidence). Repeated sessions of NIBS did not impact pain at short-term (mean difference [MD]: -0.31; P=0.23) or midterm (MD: -0.56; P=0.33; moderate quality evidence). Combining NIBS with cointerventions did not influence pain (MD: -0.31; P=0.30; moderate quality evidence). NIBS did not have a statistically significant impact on disability. DISCUSSION: There is very low-quality evidence suggesting that a single NIBS session reduces LBP intensity. In contrast, there is moderate quality evidence that repeated NIBS sessions or combination with cointervention did not improve pain or disability. Thus, current results do not support NIBS use to treat chronic LBP. Considering that tDCS was tested in 8 of 12 studies with little success, studies focusing on different NIBS techniques or innovative parameters are required to determine their potential to improve pain and disability in chronic LBP.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".