Sensitivity of lumbar spine response to follower load and flexion moment: finite element study
Bibliographic record
Abstract
The follower load (FL) combined with moments is commonly used to approximate flexed/extended posture of the lumbar spine in absence of muscles in biomechanical studies. There is a lack of consensus as to what magnitudes simulate better the physiological conditions. Considering the in-vivo measured values of the intradiscal pressure (IDP), intervertebral rotations (IVRs) and the disc loads, sensitivity of these spinal responses to different FL and flexion moment magnitudes was investigated using a 3D nonlinear finite element (FE) model of ligamentous lumbosacral spine. Optimal magnitudes of FL and moment that minimize deviation of the model predictions from in-vivo data were determined. Results revealed that the spinal parameters i.e. the IVRs, disc moment, and the increase in disc force and moment from neutral to flexed posture were more sensitive to moment magnitude than FL magnitude in case of flexion. The disc force and IDP were more sensitive to the FL magnitude than moment magnitude. The optimal ranges of FL and flexion moment magnitudes were 900-1100 N and 9.9-11.2 Nm, respectively. The FL magnitude had reverse effect on the IDP and disc force. Thus, magnitude for FL or flexion that minimizes the deviation of all the spinal parameters together from the in-vivo data can vary. To obtain reasonable compromise between the IDP and disc force, our findings recommend that FL of low magnitude must be combined with flexion moment of high intensity and vice versa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".