One motion, different strategies: Intra-individual spinal movement variability during a repeated flexion task
Bibliographic record
Abstract
Spinal movement variability is a normal feature of repetitive motions and has been hypothesized to differ between people with and without low back pain. However, normative values for intra-individual variability are currently lacking, making it difficult to judge when the variability becomes abnormal. This study used a combination of principal components analysis, single component reconstruction, and coefficient of variation to assess intra-individual variability of 3 blocks of 10 repeated spinal flexion movements in a group of 15 healthy individuals. Spinal flexion movements were assessed using motion capture cameras and a 19 × 3 matrix of retroreflective stickers on the spinous processes and bilateral paraspinal muscle bellies of S1-C7 spinal levels. All participants showed lower range of motion coefficients of variation in the lumbar spine (1.9-25.3 %) compared to the thoracic spine (7.9-30.1 %). To explain ≥80 % of the total variance within movements, 2-5 principal components were needed for each participant. Single component reconstruction revealed magnitude changes, waveform differences, and phase shifts as common sources of variability. These changes were usually observed when the coefficient of variation exceeded 10 % for that region of the spine. In conclusion, healthy individuals display varying levels of intra-individual spinal movement variability. The sources of variability can be interpreted using a combination of principal components analysis and single component reconstruction.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".