Trends and Themes in the Study of Value in Orthopedic Surgery: A Systematic Review
Bibliographic record
Abstract
Background: The study of value in orthopedic surgery aims to maximize health outcomes gained per unit cost through various health economic tools but is fragmented across various subspecialties and geographies. Therefore, it is difficult to ascertain whether this research methodology is being used to its full potential across all orthopedic subspecialties and geographies. Purpose: We sought to assess the distribution of prior health economics literature in orthopedic surgery across subspecialties and geographies. The secondary aim was to identify pertinent methodologic trends that may affect the conclusions drawn. Methods: A systematic review utilizing 3 electronic databases (Medline, Embase, and Web of Science) was performed. Inclusion criteria included prior systematic reviews assessing economic analyses across all orthopedic surgery subspecialities published between 2010 and April 24, 2021. The quality of evidence was assessed using the Assessment of Multiple Systematic Review tool. Data were qualitatively analyzed. Results: In the 44 studies included, arthroplasty (36.4%) and spine (31.8%) were the most represented subspecialties. Almost half of studies originated from the United States (45.5%), followed by the United Kingdom (18.2%). Health economic models were most commonly from the perspective of the health care or hospital system (40.5%), followed by the societal perspective (23.5%), and the payer perspective (14.8%). Conclusions: The study of value in orthopedic surgery is not uniformly leveraged across all subspecialties and geographies. Methodologically, the societal perspective was inadequately represented, despite orthopedic pathologies often incurring significant indirect costs (eg, time off work, rehabilitation expenses).
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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 | 0.150 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.036 | 0.040 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".