Sex-based differences in biomechanical function for chronic low back pain and how it relates to pain experience
Bibliographic record
Abstract
Abstract Purpose The relationship between pain experience and biomechanical impairment in chronic low back pain (LBP) is unclear. Among the broader pain literature, sex-based differences in pain experience have been established. However, it is unknown if sex-based differences in pain experience relates to compromised movement patterns for patients with chronic LBP. This study examined sex differences and whether there are sex-based associations between pain experience and biomechanical function in patients with chronic LBP. Methods To capture the biomechanical variability among LBP patients, we quantified full-body movement quality based on the extent that 3D postural trajectories deviated from matched controls during a sit-to-stand task (Kinematic Composite Score, K-Score). For both males and females, the K-Score was compared to pain measures, including patient-reported metrics and quantitative sensory testing (pressure pain threshold, PPT). Results There were significant sex-based differences in pain experience and biomechanical function in patients with LBP. Specifically, males exhibited ~ 8% lower trunk K-Scores, indicating biomechanical function that deviated more from controls when compared to female participants ( p < 0.001). However, females exhibited PPT values 29% and 41% lower than males at the control and pain sites, respectively ( p < 0.0001). There was a weak but significant negative association between PPT and K-Scores for males (R 2 = 0.14, p < 0.01), while females lacked an association. Conclusion Overall, males with LBP exhibited worse movement quality, driven by trunk motion, but higher PPTs. Possible explanations include reduced interoceptive awareness or increased kinesiophobia in males, which may influence movement patterns. This research is an initial step in uncovering the complex relationship between patient-specific factors influencing LBP disability, laying the groundwork for further exploration, and paving the way for improving outcomes with patient-specific treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".