Subgrouping People With Acute Low Back Pain Based on Psychological, Sensory, and Motor Characteristics: A Cross‐Sectional Study
Bibliographic record
Abstract
ABSTRACT Background Clustering helps identify patient subgroups with similar biopsychosocial profiles in acute low‐back pain (LBP). Motor factors are common treatment targets and are associated with disability but have not been included in acute LBP cluster development. This study aimed to identify subgroups of individuals with acute LBP based on motor, sensory and psychological characteristics and to compare these subgroups regarding clinical outcomes. Methods Ninety‐nine participants with acute LBP were recruited, and motor (bending range of motion [ROM], flexion relaxation), pain sensitivity (pressure‐pain thresholds, temporal summation of pain) and psychological factors (pain catastrophising, kinesiophobia, self‐efficacy) were measured, along with pain, disability and demographics. Results Principal component analysis accounted for 66.03% of the variance. Four component scores were entered in a hierarchical linear clustering model, deriving 3 subgroups (‘mild features’ n = 39, ‘sensorimotor’ n = 35 and ‘psychomotor’ n = 25). Between‐cluster comparisons revealed significant differences in motor, sensory and psychological variables ( p < 0.05). Sensorimotor and psychomotor clusters had higher flexion–relaxation ratios (mean difference: > 0.2), greater disability (mean difference: > 7/100) and smaller ROM (mean difference: 7 cm) compared to the ‘mild’ group. The sensorimotor cluster mostly exhibited higher temporal summation of pain (mean difference: > 1.3/10) and lower pressure‐pain thresholds (mean difference: > 1.2 kg/cm 2 ) than ‘mild’ and psychomotor clusters. The psychomotor cluster showed higher kinesiophobia (mean difference: > 6/44) and pain catastrophising (mean difference: > 12/52) than ‘mild’ and sensorimotor groups. Conclusion Findings indicate 3 subgroups, suggesting that motor factors may add granularity to acute LBP clusters. Stratified care based on these subgroups may help refine treatment pathways for acute LBP. Significance Statement Including motor factors in cluster development adds a clinically relevant metric to describe people with acute LBP and generates insight into underlying mechanisms of motor adaptation. Longitudinal testing is required to see if these subgroups are differentially related to short‐ and long‐term pain and disability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".