Health Economic Evaluations of Hip and Knee Interventions in Orthopaedic Sports Medicine: A Systematic Review and Quality Assessment
Bibliographic record
Abstract
Background: The economic burden of musculoskeletal diseases is substantial and growing. Economic evaluations compare costs and health benefits of interventions simultaneously to help inform value-based care; thus, it is crucial to ensure that studies are using appropriate methodology to provide valid evidence on the cost-effectiveness of interventions. This is particularly the case in orthopaedic sports medicine, where several interventions of varying costs are available to treat common hip and knee conditions. Purpose: To summarize and evaluate the quality of economic evaluations in orthopaedic sports medicine for knee and hip interventions and identify areas for quality improvement. Study Design: Systematic review; Level of evidence, 3. Methods: The Medline, AMED, OVID Health Star, and EMBASE databases were searched from inception to March 1, 2020, to identify economic evaluations that compared ≥2 interventions for hip and/or knee conditions in orthopaedic sports medicine. We assessed the quality of full economic evaluations using the Quality of Health Economic Studies (QHES) tool, which consists of 16 questions for a total score of 100. We classified studies into quartiles based on QHES score ( extremely poor quality to high quality) and we evaluated the frequency of studies that addressed each of the 16 QHES questions. Results: A total of 93 studies were included in the systematic review. There were 41 (44%) cost analyses, of which 21 (51%) inappropriately concluded interventions were cost-effective. Only 52 (56%) of the included studies were full economic evaluations, although 40 of these (77%) fell in the high-quality quartile. The mean QHES score was 83.2 ± 19. Authors consistently addressed 12 of the QHES questions; questions that were missed or unclear were related to statistical uncertainty, appropriateness of costing methodology, and discussion of potential biases. The most frequently missed question was whether the cost perspective of the analysis was stated and justified. Conclusion: The number of studies in orthopaedic sports medicine is small, despite their overall good quality. Yet, there are still many highly cited studies based on low-quality or partial economic evaluations that are being used to influence clinical decision-making. Investigators should follow international health economic guidelines for study design and critical appraisal of studies to further improve quality.
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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 | 0.090 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".