How non-specific low back pain affects gait kinematics: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Non-specific low back pain (NS-LBP) is a is a highly prevalent musculoskeletal condition, with an estimated 619 million prevalent cases worldwide in 2020. Alterations in spinal and lower limb dynamics are considered as potential factors directly involved in this condition, thus we carried out a systematic review to summarize the evidence regarding walking kinematics in NS-LBP. Methods: The reporting of this review followed the "2020 Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA 2020 checklist) and the protocol was preliminary registered in PROSPERO (ID: CRD42023431380). A search strategy was implemented in Medline, Embase, Scopus, Web of Science, and IEEE Xplore databases, up to March 2024. Inclusion criteria were: any analytical observational research instrumentally assessing the trunk and lower limbs kinematics of spontaneous walking in NS-LBP, in a comparison with healthy people. Study selection and data extraction were performed by two blinded reviewers, the methodological quality was evaluated by the Joanna Briggs Institute (JBI) Critical Appraisal Checklist and the quality of the evidence was rated through GRADE criteria. Results: = 0.03] in NS-LBP. Regarding motion analysis, a few authors observed a less and asymmetrical motion of the lower spine in the frontal and in the transverse plane. Conclusion: There is very-low quality evidence that gait speed, cadence and step length are reduced in patients with NS-LBP. There is proof of a movement reduction in the lower lumbar spine and in the pelvis, both in the transverse and in the frontal plane. No differences in the lower limb kinematics was consistent over the studies. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023431380.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.005 | 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".