A cross-sectional analysis of persistent low back pain, using correlations between lumbar stiffness, pressure pain threshold, and heat pain threshold
Bibliographic record
Abstract
INTRODUCTION: Little is known about the underlying biomechanical cause of low back pain (LBP). Recently, technological advances have made it possible to quantify biomechanical and neurophysiological measurements, potentially relevant factors in understanding LBP etiology. However, few studies have explored the relation between these factors. This study aims to quantify the correlation between biomechanical and neurophysiological outcomes in non-specific LBP and examine whether these correlations differ when considered regionally vs. segmentally. METHODS: This is a secondary cross-sectional analysis of 132 participants with persistent non-specific LBP. Biomechanical data included spinal stiffness (global stiffness) measured by a rolling indenter. Neurophysiological data included pain sensitivity (pressure pain threshold and heat pain threshold) measured by a pressure algometer and a thermode. Correlations were tested using Pearson's product-moment correlation or Spearman's rank correlation as appropriate. The association between these outcomes and the segmental level was tested using ANOVA with post-hoc Tukey corrected comparisons. RESULTS: A moderate positive correlation was found between spinal stiffness and pressure pain threshold, i.e., high degrees of stiffness were associated with high pressure pain thresholds. The correlation between spinal stiffness and heat pain threshold was poor and not statistically significant. Aside from a statistically significant minor association between the lower and the upper lumbar segments and stiffness, no other segmental relation was shown. CONCLUSIONS: The moderate correlation between spinal stiffness and mechanical pain sensitivity was the opposite of expected, meaning higher degrees of stiffness was associated with higher pressure pain thresholds. No clinically relevant segmental association existed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".