Spinal movement variability associated with low back pain: A scoping review
Bibliographic record
Abstract
OBJECTIVE: To identify suggestions for future research on spinal movement variability (SMV) in individuals with low back pain (LBP) by investigating (1) the methodologies and statistical tools used to assess SMV; (2) characteristics that influence the direction of change in SMV; (3) the methodological quality and potential biases in the published studies; and (4) strategies for optimizing SMV in LBP patients. METHODS: We searched literature databases (CENTRAL, Medline, PubMed, Embase, and CINAHL) and comprehensively reviewed the relevant papers up to 5 May 2020. Eligibility criteria included studies investigating SMV in LBP subjects by measuring trunk angle using motion capture devices during voluntary repeated trunk movements in any plane. The Newcastle-Ottawa risk of bias tool was used for data quality assessment. Results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. RESULTS: Eighteen studies were included: 14 cross-sectional and 4 prospective studies. Seven linear and non-linear statistical tools were used. Common movement tasks included trunk forward bending and backward return, and object lifting. Study results on SMV changes associated with LBP were inconsistent. Two of the three interventional studies reported changes in SMV, one of which was a randomized controlled trial (RCT) involving neuromuscular exercise interventions. Many studies did not account for the potential risk of selection bias in the LBP population. CONCLUSION: Designers of future studies should recognize that each of the two types of statistical tools assesses functionally different aspects of SMV. Future studies should also consider dividing participants into subgroups according to LBP characteristics, as three potential subgroups with different SMV characteristics were proposed in our study. Different task demands also produced different effects. We found preliminary evidence in a RCT that neuromuscular exercises could modify SMV, suggesting a rationale for well-designed RCTs involving neuromuscular exercise interventions in future studies.
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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 | 0.025 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".