Osteoarthritis in Italy: Impact on Health-Related Quality of Life and Health Care Resources
Bibliographic record
Abstract
OBJECTIVE: To determine how osteoarthritis (OA) severity correlates with self-reported outcomes relevant from the patient’s perspective in the Italian clinical setting.METHODS: Data were drawn from the 2017-18 Adelphi OA Disease Specific ProgrammeTM (DSP). Data were collected in the Italian clinical practice settings by primary care physicians, rheumatologists, orthopedists, and their patients with OA, during their regular office visits. Physicians completed information about OA-related visits to healthcare professionals (HCPs), tests/scans conducted, emergency room (ER) visits, surgeries, and OA-related treatment. Physicians also rated patients’ functioning on a 0 to 10 scale (0 = fully functional; 10 = completely impaired). Outcomes included Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, functional limitations, treatment needs, EuroQoL-5 Dimension (EQ-5D) visual analogue scale (VAS), the work productivity and activity impairment. Descriptive statistics (numbers and percent for categorical variables; means with standard deviations [SD] for continuous variables) were used to evaluate the different variables as appropriate.RESULTS: The study population comprised 900 patients from Italy with knee (40.9%), back (38.7%), hip (27.9%), and/or shoulder (20.3%) OA. Mean age was 66.6 years with a prevalence of female (63%) patients. Patients had mild (26%), moderate (54%), severe (20%) disease severity. Patients with severe disease reported higher functional limitations, greater use of treatments, reduced quality of life, and impaired work productivity and activity. The burdens were higher among elderly and obese patients and in patients with highest pain severity score.CONCLUSIONS: The results from this cross-sectional study show the impact of OA disease severity on all dimensions of health-related quality of life (HRQoL), as well as in OA-related health care resource use.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".