Lumbar–Pelvic Coordination Is Influenced by Lifting Task Parameters
Bibliographic record
Abstract
STUDY DESIGN: Low back kinematics, including relative lumbar and pelvic motions, were quantified during controlled lifting tasks. OBJECTIVES: To evaluate the influence of load and lifting velocity on lumbar-pelvic (LP) coordination. SUMMARY OF BACKGROUND DATA: Sagittal trunk extension is achieved through the coordinated motion of the pelvis and lumbar spine. There are no data to indicate whether lifting task design influences lumbar-pelvic coordination. METHODS: Lumbar and pelvic motions were recorded from 18 healthy subjects while performing isokinetic lifting tasks of 0.1 kg and 10 kg. Coordinated motions of the pelvis (sacral spine) and low-thoracic spine were evaluated using eigenvector analyses and a ratio of lumbar and pelvic angles (L/P). RESULTS: Eigenvector models of the lumbar-pelvic coordination accurately represented empirical coordination profiles. Weight significantly influenced lumbar-pelvic coordination. Trunk extension velocity demonstrated a small but statistically significant influence on lumbar-pelvic coordination. Weight and trunk flexion angle significantly influenced lumbar/pelvic angle ratios. CONCLUSIONS: Trunk extension was achieved through simultaneous but nonlinear contributions from both the pelvis and lumbar spine throughout the range of motion. The lumbar spine accounted for 70% of the total, with increased pelvic contributions in flexed postures. Task weight increased the lumbar contribution to total trunk motion. When performing clinical evaluations of spinal kinematics, it is necessary to recognize that unloaded motions may not fully represent loaded behavior of spinal coordination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".