Computer-assisted Total Knee Arthroplasty Is Currently of No Proven Clinical Benefit: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Navigated total knee arthroplasty (TKA) may improve coronal alignment outliers; however, it is unclear whether navigated TKA improves the long-term clinical results of TKA. QUESTIONS/PURPOSES: Does the literature contain evidence of better long-term function and lower revision rates with navigated TKA compared with conventional TKA? METHODS: A systematic literature review was conducted of navigated TKA reviewing articles related to coronal alignment, clinical knee and function scores, cost, patient satisfaction, component rotation, anteroposterior and mediolateral stability, complications, and longer-term reports. RESULTS: Coronal plane alignment is improved with navigated TKA with fewer radiographic outliers. We found limited evidence of improvements in any other variable, and function was not improved. The duration of surgery is increased and there are unique complications related to navigated TKA. The long-term benefits of additional increase in accuracy of alignment are not supported by any current evidence. CONCLUSIONS: The findings in reports of navigated TKA should be interpreted with caution. There are few short- and medium- and no long-term studies demonstrating improved clinical outcomes using navigated TKA. Despite substantial research, contradictory findings coupled with reservations about the cost and efficacy of the technology have contributed to the failure of computer navigation to become the accepted standard in TKA. Longer-term studies demonstrating improved function, lower revision rates, and acceptable costs are required before navigated TKA may be widely adopted. In the future, with improvements in study design, methodology, imaging, navigation technology, newer functional outcome tools, and longer-term followup studies, we suspect that navigated TKA may demonstrate yet unrecognized benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".