Impact of pain sensitisation on the quality of life of patients with knee osteoarthritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (PPT arm ). Pain sensitisation was classified using: (1) widespread pain, (2) lowest 10th percentile of PPT arm 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 PPT arm 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 PPT arm 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 PPT arm . 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 it