Association of Changes in Hand Pain With <scp>BMI</scp>, Employment, and Mental Well‐Being Over Four Years in Patients With Hand Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: We aimed to characterize patients with hand osteoarthritis (OA) with deteriorating or improving hand pain and to investigate patients achieving good clinical outcome after four years. METHODS: We used four-year annual Australian/Canadian Hand Osteoarthritis Index (AUSCAN) pain subscale (range 0-20) measurements from the Hand OSTeoArthritis in Secondary Care cohort (patients with hand OA). Pain changes were categorized as deterioration, stable, and improvement using the Minimal Clinical Important Improvement. Good clinical outcome was categorized using the Patient Acceptable Symptom State (PASS). Associations between baseline characteristics (patient and disease characteristics, coping styles, and illness perceptions) and outcomes were investigated using multinomial or binary logistic regression, adjusted for baseline pain, age, sex, and body mass index (BMI). RESULTS: A total of 356 patients (83% female, mean age 60.6 years, mean AUSCAN score 9.1) were analyzed. Pain improved for 38% of patients, deteriorated for 30% of patients, and remained stable for 32% of patients over four years. Four-year pain development followed annual trends. At baseline, 44% of patients reached PASS, and 49% of patients reached PASS at follow-up. Higher BMI, coping through comforting cognitions, and illness comprehension were positively associated with pain deterioration. Higher AUSCAN function score, mental well-being, and illness consequences were negatively associated with pain improvement. Employment (positive) and emotional representations (negative) were associated with both improvement and deterioration. Higher baseline AUSCAN function, tender joint count, and symptoms attributed to hand OA were associated negatively with PASS after four years. CONCLUSION: The pain course of patients with hand OA is variable, not inevitably worsening, and various factors may play a role. Whether modification of these risk factors can influence pain outcomes requires further investigation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".