Lower limb proprioception and postural control strategies in people with non-specific low back pain: A systematic review and meta-analysis
Bibliographic record
Abstract
ObjectiveTo investigate whether lower limb proprioception and postural control strategies are impaired in people with non-specific low back pain.Data sourcesA literature search was performed in the Cochrane Library, Web of Science, PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature, between January 2000 and June 2025.Review methodsStudies comparing lower limb proprioception and postural control strategies in adults with non-specific low back pain versus asymptomatic controls were included. Postural control was assessed through relative proprioception weighting, with higher values indicating greater reliance on ankle strategies and lower values indicating reliance on hip strategies. Quality appraisal utilised the Newcastle-Ottawa scale, while evidence certainty was evaluated using the grading of recommendations assessment, development and evaluation system.ResultsSeventeen studies with 1187 participants were included, of which 14 had Newcastle-Ottawa Scale scores above five. The pooled analysis showed that the non-specific low back pain group had significantly decreased ankle proprioception compared to the control group (standardised mean difference = -0.710). People with non-specific low back pain relied more on ankle strategies for postural control on both stable (weighted mean difference = 0.086) and unstable surfaces (weighted mean difference = 0.195). However, the three pooled outcome measures showed very low level of evidence due to research design and heterogeneity.ConclusionVery low level of evidence showed that people with non-specific low back pain had impaired lower limb proprioception and a higher reliance on an ankle strategy compared to asymptomatic controls, suggesting potential benefits of assessing and improving lower limb function in this population.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".