Patients With Knee Osteoarthritis Who Score Highly on the PainDETECT Questionnaire Present With Multimodality Hyperalgesia, Increased Pain, and Impaired Physical Function
Bibliographic record
Abstract
OBJECTIVES: PainDETECT is a self-report questionnaire that can be used to identify features of neuropathic pain. A proportion of patients with knee osteoarthritis (OA) score highly on the PainDETECT questionnaire. This study aimed to determine whether those with a higher "positive neuropathic" score on the PainDETECT questionnaire also had greater pain, hypersensitivity, and reduced function compared with individuals with knee OA with lower PainDETECT scores. MATERIALS AND METHODS: In total, 130 participants with knee OA completed the PainDETECT, Western Ontario and McMaster Universities Arthritis Index (WOMAC), and Pain Quality Assessment Scale questionnaires. Quantitative sensory testing was carried out at 3 sites (both knees and elbow) using standard methods. Cold and heat pain thresholds were tested using a Peltier thermode and pressure pain thresholds using a digital algometer. Physical function was assessed using 3 timed locomotor function tests. RESULTS: In total, 22.3% of participants scored in the "positive neuropathic" category with a further 35.4% in the unclear category. Participants in the "positive neuropathic" category reported higher levels of pain and more impaired function based on the WOMAC questionnaire (P<0.0001). They also exhibited increased levels of hyperalgesia at the knee and upper limb sites for all stimulation modalities except heat pain thresholds at the OA knee. They were also slower to complete 2 of the locomotion tasks. DISCUSSION: This study identified a specific subgroup of people with knee OA who exhibited PainDETECT scores in the "positive neuropathic" category. These individuals experienced increased levels of pain, widespread, multimodality hyperalgesia, and greater functional impairment than the remaining cohort. Identification of OA patients with this pain phenotype may permit more targeted and effective pain management.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".