Trends in Cost Effectiveness Analyses in Orthopaedic Surgery
Bibliographic record
Abstract
Worldwide, programs dealing with musculoskeletal health are required to set priorities and allocate resources within the constraint of limited funding. There is increasing pressure for medical technology assessment, which traditionally has involved evaluating safety and effectiveness, to also include consideration of cost effectiveness. We updated our database of orthopaedic cost-effectiveness studies, critically reviewed their methods, and examined trends over time. Current analyses have numerous shortcomings, such as the inclusion of relatively few studies, inconsistent methodologic approaches, and lack of transparency. The wide variation in cost-effectiveness ratios observed among current interventions suggests efficiency can be improved. Despite reimbursement authorities in many other countries formally considering cost-effectiveness when determining coverage of new technologies, Medicare has been resistant to considering costs of treatments. Regardless of this policy deficiency, conducting cost-effectiveness analyses represents a prudent step forward in illuminating the tradeoffs involved in difficult resource allocation decisions, and there is an urgent need to consider economic impact in future studies using standardized and transparent methods.
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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 | 0.065 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.033 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".