Health economics in the field of osteoarthritis: An Expert's consensus paper from the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO)
Bibliographic record
Abstract
OBJECTIVES: There is an important need to evaluate therapeutic approaches for osteoarthritis (OA) in terms of cost-effectiveness as well as efficacy. METHODS: The ESCEO expert working group met to discuss the epidemiological and economic evidence that justifies the increasing concern of the impact of this disease and reviewed the current state-of-the-art in health economic studies in this field. RESULTS: OA is a debilitating disease; it is increasing in frequency and is associated with a substantial and growing burden on society, in terms of both burden of illness and cost of illness. Economic evaluations in this field are relatively rare, and those that do exist, show considerable heterogeneity of methodological approach (such as indicated population, comparator, decision context and perspective, time horizon, modeling and outcome measures used). This heterogeneity makes comparisons between studies problematic. CONCLUSIONS: Better adherence to guidelines for economic evaluations is needed. There was strong support for the definition of a reference case and for what might constitute "standard optimal care" in terms of best clinical practice, for the control arms of interventional studies.
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 | 0.079 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".