A patients’ view of OA: the Global Osteoarthritis Patient Perception Survey (GOAPPS)
Bibliographic record
Abstract
Abstract Background Globally, osteoarthritis (OA) is the third condition associated with disability. There is still poor treatment in OA but science holds the key to finding better treatments and a cure. It is essential to learn what’s important to patients from them to implement the most effective OA management. The OA Patients Task Force, conducted the Global OA Patient Perception Survey (GOAPPS)-the first global survey to compare the quality of life (QoL) & patient perceptions of care across countries. The goal was to collect data on OA patients' perception of OA to understand patients’ needs and expectations to improve OA management.Methods Observational, cross-sectional study by online survey data collection into three languages. Patient demographics, symptomology, OA impact on daily activity and QoL data were collected. The questionnaire comprised of 4 sections: clinical characteristics, relationship with physicians, perception of attention, treatment, information, and QoL.Results A total of 1512 surveys were filled in 7 countries. 84.2% of respondents reported pain/tenderness and 91.1% experienced limitations to physical activities. 42.3% of patients were not satisfied with their current OA treatment. 86% had comorbidities, especially hypertension, and obesity. 51.3% and 78% would like access to additional drug or additional non-drug/non-surgical treatments respectively. 51.7% considered their QoL satisfactory.Conclusions OA has a significant impact on patients’ daily activities and the desire to play an active role in managing their disease. Patients seek additional treatments stressing the need for investing in clinical research, implementing OA preventive measures and managing interventions to improve the healthcare value chain in OA.
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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 | 0.006 |
| 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 | 0.001 |
| 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".