Decision Analysis in the Management of Hip and Knee Osteoarthritis: A Systematic Review
Bibliographic record
Abstract
Decision analysis is an increasingly used tool to guide policymakers and clinicians toward objective decision-making in uncertain clinical scenarios by analyzing the cost-effectiveness and health benefits of treatment modalities. In this systematic review, we summarize and appraise the current use of decision analysis in the management of hip and knee osteoarthritis (OA), the leading source of disability and societal costs in patients over 70. Publications involving decision analysis modelling for hip and knee OA between 1995 and 2021 were included. Among 54 included studies, there were 33 knee- and 18 hip OA-related models, while three were overlapping. Included articles primarily used Markov decision models (39), followed by simple decision trees (eight), microsimulations (five), and discrete event simulations (two) to compare OA treatment modalities. Models most commonly compared surgical procedures and devices (21), surgical versus nonoperative management (12), intraarticular injections (seven), and rehab therapies (five). More than half of all included studies (33) were published in the last five years. This study finds that there has been a large increase in the publication of hip and knee OA-related decision analysis models, particularly over the most recent five years. High-quality decision analysis models incorporate sensitivity and value of information analyses and take on broader, societal perspectives to incorporate utilities, direct costs, and indirect costs of management decisions. Surgeons should be familiar with the principles of decision analysis, which can be used to guide complicated real-world decision-making by incorporating risks and benefits of multiple strategies.
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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 | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".