Dispositional traits help explain individual differences in relationships between a radiographic knee osteoarthritis measure, pain, and physical function
Bibliographic record
Abstract
Background:The concordance between radiograph-derived Kellgren–Lawrence (KL) scores for knee osteoarthritis (KOA) and experimental and clinical pain and KOA-related physical function is conflicting.Objectives:We investigate whether the inclusion of dispositional traits reduces variability between KOA radiographic findings, experimental pain, clinical pain, and function in individuals with knee pain.Design:This study is a cross-sectional, secondary analysis of data collected from the UPLOAD-II study.Methods:Adults aged 45–85 years with and without knee pain were enrolled. Data collected included sociodemographics, knee radiographs, experimental pain, clinical pain and function, and trait affect. Vulnerable and protective dispositional traits were classified from combined positive and negative trait affect measures. KL scores were determined from the knee radiographs. Unadjusted and adjusted (age, sex, comorbidities, and body mass index) regression analyses were completed with SAS version 9.4 (Cary, NC, USA).Results:The study included 218 individuals with a mean age of 58 years, 63.6% women, and 48.2% non-Hispanic black adults. Dispositional traits were associated with the experimental pain measures. No association between radiographic KOA and experimental pain was observed. In a combined and adjusted analysis, dispositional traits were predictive of knee punctate pain temporal summation (p = 0.0382). Both dispositional traits and radiographic KOA scores independently and combined were predictive of Graded Chronic Pain Scale pain and function, and Western Ontario and McMaster University pain and function (ps ⩽ 0.01). Improvements in R2 were noted across all models with the inclusion of dispositional traits.Conclusion:Consideration of dispositional traits reduces the variability between radiographic KOA and pain and function. Non-pathological and associated pain-related psychological factors and dispositional traits might serve as parsimonious proxy tools to improve clinical assessments.Registration:N/A.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".