Sensorimotor Integration in Chronic Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: Chronic low back pain (CLBP) impacts on spine movement. Altered sensorimotor integration can be involved. Afferents from the lumbo-pelvic area might be processed differently in CLBP and impact on descending motor control. This study aimed to determine whether afferents influence the corticomotor control of paravertebral muscles in CLBP. Fourteen individuals with CLBP (11 females) and 13 pain-free controls (8 females) were tested with transcranial magnetic stimulation (TMS) to measure the motor-evoked potential [MEP] amplitude of paravertebral muscles. Noxious and non-noxious electrical stimulation, and magnetic stimulation in the lumbo-sacral area were used as afferent stimuli and triggered 20 to 200 ms prior to TMS. EMG modulation elicited by afferent stimulation alone was measured to control net motoneuron excitability. MEP/EMG ratio was used as a measure of corticospinal excitability with control of net motoneuron excitability. MEP/EMG ratio was larger at 60, 80 and 100-ms intervals in CLBP compared to controls, and afferent stimulations alone reduced EMG amplitude greater in CLBP than controls at 100 ms. Our results suggest alteration in sensorimotor integration in CLBP highlighted by a greater facilitation of the descending corticospinal input to paravertebral muscles. Our results can help to optimise interventions by better targeting mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".