The Relationship Between Clinical and Quantitative Measures of Pain Sensitization in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Pain sensitization in knee osteoarthritis (OA) is associated with greater symptom severity and poorer clinical outcomes. Measures that identify pain sensitization and are accessible to use in clinical practice have been suggested to enable more targeted treatments. This merits further investigation. This study examines the relationship between quantitative sensory testing (QST) and clinical measures of pain sensitization in people with knee OA. METHODS: A secondary analysis of data from 134 participants with knee OA was performed. Clinical measures included: manual tender point count (MTPC), the Central Sensitization Inventory (CSI) to capture centrally mediated comorbidities, number of painful sites on a body chart, and neuropathic pain-like symptoms assessed using the modified PainDetect Questionnaire. Relationships between clinical measures and QST measures of pressure pain thresholds (PPTs), temporal summation, and conditioned pain modulation were investigated using correlation and multivariable regression analyses. RESULTS: Fair to moderate correlations, ranging from -0.331 to -0.577 (P<0.05), were identified between MTPC, the CSI, number of painful sites, and PPTs. Fair correlations, ranging from 0.28 to 0.30 (P<0.01), were identified between MTPC, the CSI, number of painful sites, and conditioned pain modulation. Correlations between the clinical and self-reported measures and temporal summation were weak and inconsistent (0.09 to 0.25). In adjusted regression models, MTPC was the only clinical measure consistently associated with QST and accounted for 11% to 12% of the variance in PPTs. DISCUSSION: MTPC demonstrated the strongest associations with QST measures and may be the most promising proxy measure to detect pain sensitization clinically.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".