Pain Catastrophizing Is Associated With Health‐Related Quality of Life in Patients With Hip Osteoarthritis: A Multicenter Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the relationship between pain catastrophizing (PC) and health-related quality of life (HRQoL), while accounting for pain intensity and other factors in patients with hip osteoarthritis (OA). METHODS: This multicenter, cross-sectional study included a total of 160 participants and was conducted at five hospitals in Japan. The primary outcome was the HRQoL status, which was assessed using the Japanese version of the 12-item Short Form. Physical (PCS-12) and mental (MCS-12) component summary scores were used as dependent variables. Age, sex, body mass index (BMI), affected side, hip OA severity, bilateral range of motion (ROM), muscle strength, pain intensity, and PC scale scores were measured as independent variables. After screening, multiple regression analysis was performed for each outcome. RESULTS: Higher BMI (β = -0.17, p < 0.05), higher hip flexion ROM on the unaffected side (β = -0.26, p < 0.05), lower hip flexion ROM on the affected side (β = 0.22, p < 0.05) and higher PC scale score (β = -0.28, p < 0.05) were associated with worse PCS-12. In addition, higher BMI (β = -0.18, p < 0.05) and higher PC scale scores (β = -0.29, p < 0.05) were associated with worse MCS-12 after accounting for confounding factors. CONCLUSION: This study suggested that PC is an issue in patients with hip OA and is a potential target for interventions aimed at improving HRQoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".