Use of IMMPACT Recommendations to Explore Pain Phenotypes in People with Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (OA) is a disease of multiple phenotypes of which a chronic pain phenotype (PP) is known. Previous PP studies have focused on one domain of pain and included heterogenous variables. We sought to identify multidimensional PPs using the IMMPACT recommendations and their relationship to clinical outcomes. METHODS: Participants >40 years of age with knee OA having a first-time orthopedic consultation at five university affiliated hospitals in Montreal, Quebec, and Hamilton (Canada) were recruited. Latent profile analysis was used to determine PPs (classes) using variables recommended by IMMPACT. This included pain variability, intensity and qualities, somatization, anxiodepressive symptoms, sleep, fatigue, pain catastrophizing, neuropathic pain, and quantitative sensory tests. We used MANOVA and χ2 tests to assess differences in participant characteristics across the classes and linear and Poisson regression to evaluate the association of classes to outcomes of physical performance tests, self-reported function and provincial healthcare data. RESULTS: In total, 343 participants were included (mean age 64 years, 64% female). Three classes were identified with increasing pain burden (class3 > class1), characterized by significant differences across most self-report measures and temporal summation, and differed in terms of female sex, younger age, lower optimism and pain self-efficacy. Participants in class2 and class3 had significantly worse self-reported function, stair climb and 40 m walk tests, and higher rates of healthcare usage compared to those in class1. CONCLUSIONS: Three distinct PPs guided by IMMPACT recommendations were identified, predominated by self-report measures and temporal summation. Using this standardized approach may improve PP study variability and comparison.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".