The Mediating Effect of Central Sensitization on the Relation between Pain Intensity and Psychological Factors: A Cross-Sectional Study with Mediation Analysis
Bibliographic record
Abstract
Background: Central sensitization (CS) and psychological factors are associated with pain intensity; however, the mediating role of CS on the relation between psychological factors and pain intensity remains unclear. Objectives: We performed mediation analysis to investigate how CS mediates relation between psychological factors and pain intensity. Methods: Twenty patients with musculoskeletal pain were included in this cross-sectional study. Central sensitization inventory (CSI), one pain intensity-related outcome measure (Short-Form McGill Pain Questionnaire 2 (SFMPQ2)), and three psychological outcome measures (Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Scale-4 (PCS), and Tampa Scale for Kinesiophobia-11 (TSK)) of all participants were assessed. The mediation analysis with a bootstrap sampling procedure was used to assess the indirect effects. The level of significance was set at 5%. Results: Mediation analysis showed that the HADS-anxiety, HADS-depression, and PCS had significant indirect effects on the pain ratings of CSI. Additionally, the direct effect was significant only for PCS. Conclusions: The relationship among anxiety symptoms, depression symptoms, and pain intensity was completely mediated by CS. Furthermore, the relationship between catastrophic thinking and pain intensity was partially mediated by CS. Our findings suggest that CS mediates relation between psychological factors and pain intensity, and CS-focused intervention may be important.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".