Cost of Illness and Determinants of Costs Among Patients with Gout
Bibliographic record
Abstract
OBJECTIVE: To estimate costs of illness in a cross-sectional cohort of patients with gout attending an outpatient rheumatology clinic, and to evaluate which factors contribute to higher costs. METHODS: Altogether, 126 patients with gout were clinically assessed. They completed a series of questionnaires. Health resource use was collected using a self-report questionnaire that was cross-checked with the electronic patient file. Productivity loss was assessed by the Work Productivity and Activity Impairment Questionnaire, addressing absenteeism and presenteeism. Resource use and productivity loss were valued by real costs, and annual costs per patient were calculated. Factors contributing to incurring costs above the median were explored using logistic univariable and multivariable regression analysis. RESULTS: Mean (median) annual direct costs of gout were €5647 (€1148) per patient. Total costs increased to €6914 (€1279) or €10,894 (€1840) per patient per year when adding cost for absenteeism or both absenteeism and presenteeism, respectively. Factors independently associated with high direct and high indirect costs were a positive history of cardiovascular disease, functional limitations, and female sex. In addition, pain, gout concerns, and unmet gout treatment needs were associated with high direct costs. CONCLUSION: The direct and indirect costs-of-illness of gout are primarily associated with cardiovascular disease, functional limitations, and female sex.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".