Systematic review and meta-analysis of economic and healthcare resource utilization outcomes for robotic versus manual total knee arthroplasty
Bibliographic record
Abstract
The introduction of robotics in orthopedic surgery has led to improved precision and standardization in total knee arthroplasty (TKA). Clinical benefits of robotic versus manual TKA have been well established; however, evidence for economic and healthcare resource utilization outcomes (HRU) is lacking. The primary objective of this study was to compare economic and HRU outcomes for robotic and manual TKA. The secondary objective was to explore comparative robotic and manual TKA pain and opioid consumption outcomes. Multi-database literature searches were performed to identify studies comparing robotic and manual TKA from 2016 to 2022 and meta-analyses were conducted. This review included 50 studies with meta-analyses conducted on 35. Compared with manual TKA, robotic TKA was associated with a: 14% reduction in hospital length of stay (P = 0.022); 74% greater likelihood to be discharged to home (P < 0.001); and 17% lower likelihood to experience a 90-day readmission (P = 0.043). Robotic TKA was associated with longer mean operating times (incision to closure definition: 9.27 min longer, P = 0.030; general operating time definition: 18.05 min longer, P = 0.006). No differences were observed for total procedure cost and 90-day emergency room visits. Most studies reported similar outcomes for robotic and manual TKA regarding pain and opioid use. Coupled with the clinical benefits of robotic TKA, the economic impact of using robotics may contribute to hospitals' quality improvement and financial sustainability. Further research and more randomized controlled trials are needed to effectively quantify the benefits of robotic relative to manual TKA.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".