Peripheral Neurostimulation and Specific Motor Training of Deep Abdominal Muscles Improve Posturomotor Control in Chronic Low Back Pain
Bibliographic record
Abstract
OBJECTIVES: Chronic low back pain (CLBP) is associated with an impaired control of transversus abdominis/internal oblique muscle (TrA/IO), volitionally and during anticipatory postural adjustment (delay) along with maladaptive reorganization of primary motor cortex (M1). Specific training of deep trunk muscles and repetitive peripheral magnetic stimulation (RPMS) improve motor control. We thus tested whether RPMS over TrA/IO combined with training could promote TrA/IO motor control and decrease pain beyond the gains already reached in CLBP. METHODS: Thirteen CLBP patients, randomly allocated to RPMS and sham groups and compared with 9 pain-free controls, were tested in 1 session before/after (stimulation alone) and after (stimulation+TrA/IO training) combination. TrA/IO motor patterns were recorded during ballistic shoulder flexion using surface electromyography. Transcranial magnetic stimulation tested M1 excitability and short-interval intracortical inhibition. A blinded physical therapist assessed pain, disability, and kinesiophobia. RESULTS: The missing short-interval intracortical inhibition in CLBP was restored by RPMS alone then reduced after combination of RPMS with training. This combination also normalized the (at-first delayed) anticipatory activation of iTrA/IO (ipsilateral to arm raised) and the (at-first shortened) TrA/IO coactivation duration. Sham did not influence. Pain was reduced in both groups but kinesiophobia was decreased only in RPMS 2 weeks later. CONCLUSIONS: This study supports that peripheral neurostimulation (adjuvant to training) could improve TrA/IO motor learning and pain in CLBP associated with motor impairment. Testing of enlarged samples over several sessions should question the long-term influence of this new approach in CLBP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".