Variability of spinal stiffness and its relation to daily activities: A prospective cohort study using a mechanical assisted indentation technique
Bibliographic record
Abstract
BACKGROUND: Spinal stiffness is a potentially important cause of spinal pain but we have limited knowledge of its variability in relation to daily activities. This study investigated the association between variability of spinal stiffness and body anthropometry, age, and different daily activities in a cohort of healthy participants. METHODS: A cross-sectional study of a cohort of 25 healthy participants (median age 24 years; 52% male) was employed to collect stiffness measurements obtained through surface indentation of the lumbar and thoracic spine three times over the course of a single day. Daily activities (sitting, standing, and movement) were assessed using accelerometer data. Linear mixed models analyzed the associations between stiffness variability and body anthropometry, age, and activities. FINDINGS: We observed significant variability in spinal stiffness among individuals, with greater variability in the lumbar region compared to the thoracic region. Both sedentary and standing activities influenced stiffness variability at a statistically significant level. However, body anthropometry and age was not associated with stiffness variability. INTERPRETATION: Our study demonstrates that daily activities (i.e., sedentary and standing) contribute to the variability of spinal stiffness during the day. Age and sex did not significantly impact stiffness variability. Further investigations are warranted to explore the clinical implications of stiffness changes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".