Experimental Pain Phenotypes in Older Adults with Knee Osteoarthritis: A Neural Network-Based Clustering Approach
Bibliographic record
Abstract
Abstract Research has emphasized the “phenotyping” of knee osteoarthritis (KOA) pain as a priority to effectively target therapies to individual patients. Following this initiative, this study aimed to characterize pain phenotypes based on experimental pain responses in older adults with symptomatic KOA. We utilized baseline data from a clinical trial that combined non-invasive neuromodulation and meditation in its multimodal approach (N = 200). Participants completed demographic and clinical questionnaires, followed by a multimodal quantitative sensory testing (QST) battery. For phenotyping, we implemented a two-layer neural network-based k-means algorithm. Four phenotypes emerged, showing significant differences across QST measures (p < 0.001) and were characterized as: (1) high pressure pain thresholds and high conditioned pain modulation (indicating low sensitivity to pain and efficient descending inhibition); (2) average pain responses across most QST modalities; (3) low pressure pain thresholds, high punctate mechanical pain, enhanced temporal summation of pain, and low conditioned pain modulation (indicating full manifestation of peripheral, spreading, and central sensitization along with deficient descending inhibition); and (4) low heat pain thresholds, low heat pain tolerance, and high cold pain (indicating high sensitivity to thermal stimuli). Phenotypes differed by gender, marital status, pain severity (measured by the numeric rating scale), and KOA-related symptoms (measured by the Western Ontario and McMaster Universities Osteoarthritis Index) (p < 0.05). Our findings suggest that older adults with symptomatic KOA should be treated according to their phenotypes. Additionally, the identified phenotypes may be useful for selecting and stratifying patients in clinical trials evaluating analgesic compounds and non-pharmacological interventions.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".