A Comparison of Clinical Spinal Mobility Measures to Experimentally Derived Lumbar Spine Passive Stiffness
Bibliographic record
Abstract
Spinal stiffness and mobility assessments vary between clinical and research settings, potentially hindering the understanding and treatment of low back pain. A total of 71 healthy participants were evaluated using 2 clinical assessments (posteroanterior spring and passive intervertebral motion) and 2 quantitative measures: lumped mechanical stiffness of the lumbar spine and local tissue stiffness (lumbar erector spinae and supraspinous ligament) measured via myotonometry. The authors hypothesized that clinical, mechanical, and local tissue measures would be correlated, that clinical tests would not alter mechanical stiffness, and that males would demonstrate greater lumbar stiffness than females. Clinical, lumped mechanical, and tissue stiffness were not correlated; however, gradings from the posteroanterior spring and passive intervertebral motion tests were positively correlated with each other. Clinical assessments had no effect on lumped mechanical stiffness. The males had greater lumped mechanical and lumbar erector spinae stiffness compared with the females. The lack of correlation between clinical, tissue, and lumped mechanical measures of spinal stiffness indicates that the use of the term "stiffness" by clinicians may require reevaluation; clinicians should be confident that they are not altering mechanical stiffness of the spine through segmental mobility assessments; and greater resting lumbar erector stiffness in males suggests that sex should be considered in the assessment and treatment of the low back.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".