Determination and characterization of patient subgroups based on pain trajectories in hand osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To investigate pain, pain trajectories and their determinants in hand osteoarthritis (OA). METHODS: Data from the HOSTAS (Hand OSTeoArthritis in Secondary care) consisting of consecutive hand OA patients were used. Australian Canadian Osteoarthritis Hand Index (AUSCAN) pain was measured yearly for four years. Patients with complete AUSCAN at ≥2 time points were eligible for longitudinal analysis. Associations between variables of interest and baseline AUSCAN pain were investigated with linear regression. Development of pain over time was modelled using latent class growth analysis (LCGA). Associations of LCGA classes with variables of interest were analysed using multinomial logistic regression adjusted for baseline pain. RESULTS: A total of 484/538 patients [mean (s.d.) age 60.8 (8.5) years, 86% women, mean (s.d.) AUSCAN pain 9.3 (4.3)] were eligible for longitudinal analysis. Sex, marital and working status, education, disease duration and severity, anxiety and depression scores, lower health-related quality of life (HR-QoL), specific illness perceptions and coping styles were associated with baseline pain. LCGA yielded three classes, characterized by average pain levels at baseline; average pain remained stable over time within classes. Classes with more pain were positively associated with BMI, tender joint count, symptom duration, hand function scores and depression scores, negatively with physical HR-QoL, and education level. CONCLUSION: Baseline pain was associated with patient and disease characteristics, and psychosocial factors. LCGA showed three pain trajectories in hand OA patients, with different baseline pain levels and stable pain over time. Classes were distinguished by BMI, education level, disease severity, depression and HR-QoL.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".