Impact of pain sensitisation on the quality of life of patients with knee osteoarthritis
Bibliographic record
Abstract
Objectives We aim to evaluate the effect on different ways of classifying pain sensitisation on impact and quality of life (QoL) in knee osteoarthritis (KOA). Methods We used baseline data from a cohort of consecutive patients with KOA listed for arthroplasty. We collected demographics and number of painful body sites. We measured pressure pain thresholds at the right forearm (PPTarm). Pain sensitisation was classified using: (1) widespread pain, (2) lowest 10th percentile of PPTarm and (3) PainDETECT questionnaire ≥13/38. Impact and QoL were assessed using Western Ontario and McMaster Universities Osteoarthritis Index and Short Form-36. Impact and QoL scores in patients with or without pain sensitisation were compared. We evaluated the association of pain sensitisation measures with QoL scores using multivariable regression. Results 233 patients (80% female, mean age 66 years) included in the analysis; 7.3%, 11.6% and 4.7% were classified as having pain sensitisation by widespread pain, low PPTarm and PainDETECT criteria, respectively. There was minimal overlap of patients as classified as pain sensitisation phenotype by different measures. Patients with pain sensitisation had poorer QoL compared with those without. Low PPTarm identified patients with poorer general health, while widespread pain and PainDETECT identified poorer QoL in more psychological domains. There was weak correlation between number of painful body sites and PainDETECT (rho=0.23, p<0.01), but no significant correlation with PPTarm. Conclusion Patients with KOA with pain sensitisation have poorer QoL compared with those without, regardless of classification method. Different criteria defined patients with different pattern of QoL impact.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".