Examining Pain Phenotyping and the Role of Adipose Tissue in the Early Stage and Across the Disease Course of Knee Osteoarthritis
Bibliographic record
Abstract
Phenotyping in the early stages of knee osteoarthritis (OA) based on pain mechanisms may help to predict individualized prognosis and develop subgroup-specific treatments towards preventing symptomatic worsening. However, no phenotyping studies have been conducted in the early stages of OA, and how or if these phenotypes change over time is not known. Also, adipose tissue has been associated with systemic inflammation that can contribute to pain. To better understand different phenotypes, their association with pain worsening, their stability, and the role of adiposity in knee OA we conducted this thesis spanning three studies. In the first study, we identified different models of phenotypes based on pain-related (e.g. measures of pain sensitization and psychological factors) and pain plus disease-related (e.g. comorbidities, muscle strength) variables in early-stage knee OA. We found that none of the derived phenotypes were associated with pain worsening at two-year follow-up. In the second study, we identified different pain phenotypes using multidimensional pain characteristics in those with knee pain and at risk of knee OA, and these characteristics remained stable throughout the 7-year follow-up. The phenotypes differed in the response to pain measures (study 1 and 2), muscle strength, comorbidities, and gait features (study 1). In the third study, we identified that adiposity was not significantly associated with pain intensity and synovitis in people with knee OA and that markers of systemic immune inflammation do not have a moderating effect on this association when adjusted for confounding variables. Together, these results have implications for classifying patients in the early stages of the disease, explaining the course of different pain phenotypes before disease onset, and suggests a conflicting role of adipose tissue in OA pain/synovitis. In addition, our results guide further research towards external validation of phenotypes and determining the causal relationships between adiposity and symptoms in knee OA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".