Correlations Between Individuals’ Characteristics and Spinal Stiffness in Individuals With and Without Back Pain: A Combined Analysis of Multiple Data Sets
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to describe the correlations between individual characteristics and spinal stiffness as measured with different spinal stiffness measurement devices in individuals with and without back pain. METHODS: A secondary analysis of 3 adult data sets obtained using 3 different devices, in 2 spinal regions, from a total of 5 separate cross-sectional studies was conducted. Differences in spinal stiffness between men and women and in the strength of correlations among spinal stiffness and age and anthropometric characteristics were evaluated using either the t test for independent samples, Pearson's correlation coefficient, or Kendall's τ rank correlation coefficient. RESULTS: As expected, results varied between data sets; however, few factors had consistent correlations. Specifically, spinal stiffness was significantly lower in women than men in all 3 data sets. Height was positively correlated with spinal stiffness across all data sets. Although weight was correlated with thoracic stiffness, its correlation with lumbar stiffness varied. In 2 data sets, body mass index was inversely associated with lumbar spinal stiffness, whereas results from the thoracic spine region revealed a positive correlation. The results for 1 data set suggest that physiological measurement evaluating body weight distribution may also affect spinal stiffness; however, the specific correlation remains unclear. CONCLUSION: Despite data set differences, significant correlations were observed, indicating that participants' characteristics appear to affect spinal stiffness measurement.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".