Paraspinal Muscle Passive Stiffness Remodels in Direct Response to Spine Stiffness
Bibliographic record
Abstract
STUDY DESIGN: Basic science study of the relationship between the structural properties of the spine and its surrounding musculature. OBJECTIVE: To determine whether an increase in spine stiffness causes an inverse compensatory change in the passive stiffness of the adjacent paraspinal muscles. SUMMARY OF BACKGROUND DATA: Intervertebral disc degeneration causes an increase in multifidus passive stiffness; this was hypothesized to compensate for a decrease in spine stiffness associated with disc degeneration. Mice lacking equilibrative nucleoside transporter 1 (ENT1) develop progressive ectopic calcification of the fibrous connective tissues of the spine, which affects the lumbar spine by 6 months of age and likely creates a mechanically stiffer spine. METHODS: Experiments were conducted on four groups of mice (n = 8 mice/group): wild-type (WT) and ENT1 knockout (KO) at 2 or 8 months of age. Lumbar spines were removed and tested in cyclic axial compression to determine neutral zone length and stiffness. Single muscle fibers and bundles of fibers were isolated from lumbar multifidus and erector spinae, as well as tibialis anterior (a non-spine-related control) and tested to determine elastic modulus (passive stiffness). RESULTS: At 2 months of age, neither spine nor muscle stiffness was different between KO and WT. At 8 months of age, compared with WT the lumbar spines of ENT1 KO mice had a stiffer and shorter neutral zone, and the paraspinal muscle fibers were less stiff; however, fiber bundles were not different. In addition, tibialis anterior was not different between KO and WT. CONCLUSION: This work has confirmed that calcification of spinal connective tissues in the ENT1 KO mouse results in a stiffened spine, whereas the concurrent decrease in muscle fiber elastic modulus in the adjacent paraspinal muscles suggests a direct compensatory relationship between the stiffness of the spine and the muscles that are attached to it. LEVEL OF EVIDENCE: N/A.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".