Moderate to Severe Osteoarthritis Pain and Its Impact on Patients in the United States: A National Survey
Bibliographic record
Abstract
Purpose: Osteoarthritis (OA) is one of the most common causes of chronic pain and a leading cause of disability in the US. The objective of this study was to examine the clinical and economic burden of OA by pain severity. Patients and Methods: We used nationally representative survey data. Adults ≥ 18 years with self-reported physician-diagnosed OA and experiencing OA pain were included in the study. OA pain severity was measured using the Short Form McGill Pain Questionnaire Visual Analog Scale (SF-MPQ-VAS). Data were collected for demographics, clinical characteristics, health-related quality of life (HRQoL), productivity, OA treatment, adherence to pain medication, and healthcare resource utilization. Univariate analysis was performed to examine differences between respondents with moderate-to-severe OA pain vs those with mild OA pain. Results: Higher proportions of respondents with moderate-to-severe OA pain (n=3798) compared with mild OA pain (n=2038) were female (69.4% vs 57.3%), < 65 years of age (54.8% vs 43.4%), and not employed (70.6% vs 64.5%). Respondents with moderate-to-severe OA pain experienced OA pain daily (80.8% vs 48.8%), were obese (53.0% vs 40.5%), had more comorbidities (sleep disturbance, insomnia, depression, and anxiety), and reported significantly poorer health status and HRQoL, and greater productivity and activity impairment (all P < 0.05). Moderate-to-severe OA pain respondents were prescribed significantly more pain medications than mild OA pain respondents (41.0% vs 17.0%) and had higher adherence (75.9% vs 64.1%) yet were less satisfied with their pain medications (all P < 0.001). Outpatient and emergency room visits, and hospitalizations in the 6 months prior to the survey were significantly higher in moderate-to-severe OA pain respondents vs those with mild OA pain (all P < 0.05). Conclusion: Patient and clinical burden was significantly greater in moderate-to-severe OA pain respondents vs mild OA pain respondents and may inform decision-making for appropriate resource allocation and effective management strategies that target specific subgroups. Keywords: osteoarthritis, moderate-to-severe pain, pain severity, health-related quality of life, HRQoL, healthcare resource utilization, HCRU, medication adherence, treatment satisfaction
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".