The Relationship Between Hip Flexion/Extension and the Sagittal Curves of the Spine
Bibliographic record
Abstract
The objective of this study was to develop a finite element model (FEM) in order to study the relationship between hip flexion/extension and the sagittal curves of the spine. A previously developed FEM of the spine, rib cage and pelvis personalized to the 3D reconstructed geometry of a patient using biplanar radiographs was adapted to include the lower limbs including muscles. Simulations were performed to determine: the relationship between hip flexion / extension and lumbar lordosis / thoracic kyphosis, the mechanism of transfer between hip flexion / extension and pelvic rotation, and the influence that knee bending, muscle stiffness, and muscle mass have on the degree to which sagittal spinal curves are modified due to lower limb positioning. Preliminary results showed that the model was able to accurately reproduce published results for the modulation of lumbar lordosis due to hip flexion; which proved to linearly decrease 68% at 90 degrees of flexion. Additional simulations showed that the hamstrings and gluteal muscles were responsible for the transmission of hip flexion to pelvic rotation with the legs straight and flexed respectively, and the important influence of knee bending on lordosis modulation during lower limb positioning. The knowledge gained through this study is intended to be used to improve operative patient positioning.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".