Trends in Operative Time and Short-Term Outcomes After Conventional and Navigated Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Adoption of navigated total knee arthroplasty (Nav-TKA) is increasing. However, it has been suggested that a perceived decrease in surgical efficiency and a lack of proven superior functional outcomes associated with Nav-TKA have hindered its widespread adoption. METHODS: The American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who had undergone TKA with or without navigation between 2012 and 2018. Patients were further subclassified based on the type of navigation used, image-guided or imageless. Multivariate logistic regression was used to compare operative time and 30-day complication rates between conventional TKA (Conv-TKA) and Nav-TKA with and without image guidance. RESULTS: A total of 316,210 Conv-TKAs and 8554 Nav-TKAs (8270 imageless, 284 image-guided) were identified. Across the study period, the use of Nav-TKA was associated with a 1.5-minute increase in operative time. However, the overall time burden decreased over the study period, and by 2018, the mean operative time for Nav-TKA was 2.4 minutes less than that of Conv-TKA. Compared with Conv-TKA, Nav-TKA was associated with decreased rates of postoperative transfusion and surgical site complications but a similar incidence of systemic thromboembolism. CONCLUSIONS: This is the first large-scale database study to examine the differences in operative time between Conv-TKA and Nav-TKA. The time burden associated with Nav-TKA decreased over the study period and even reversed by 2018. Nav-TKA was associated with lower rates of postoperative transfusion and surgical site complications. Further studies are needed to evaluate the long-term and functional outcomes between conventional and navigated knee arthroplasty techniques.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".