Can summary measures of magnitude and structure of trunk movement variability differentiate between people with and without chronic low back pain?
Bibliographic record
Abstract
BACKGROUND: There is inconsistent evidence suggesting that people with chronic low back pain may differ in variability of repeated trunk movements compared to people without chronic low back pain. These inconsistencies may be due to low reliability and task dependence of movement variability measures, which can be addressed using multiple movement tasks and summary measures. METHODS: Participants with and without chronic low back pain were recruited. Measurement sessions involved 30 repetitions of movements in the sagittal, transverse, and combined planes. Kinematics of the pelvis, thorax, and lumbar spine were estimated using inertial sensors placed on the sacrum and thorax. Magnitude of movement variability was quantified as the mean standard deviation of Euler angles for the thorax, pelvis, and lumbar spine across normalized cycles for each movement axes, resulting in 27 variables. Additionally, structure of variability was assessed using Lyapunov exponents for local dynamic stability, yielding 9 additional variables. Principal Component Analysis reduced the dimensionality of each variability measure (magnitude and structure). Stepwise logistic regression with principal component scores tested for differences between groups. FINDINGS: In the magnitude of variability analysis, four principal components were retained. The first two principal components significantly differentiated between people with low back pain and controls, accounting for 32.5 % and 14 % of the total variance, respectively. In the structure of variability analysis, no principal components were found to significantly contribute to differentiating between the two groups. INTERPRETATION: Summary measures of the magnitude, but not the structure, of trunk movement variability differentiated between people with and without chronic low back pain. CLINICAL TRIAL: NCT02059317, CPP: 2013.11.09bis Sud Méditerranée III, N° RCB: 2013-A01379-36.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".