Navigated Versus Conventional Total Knee Arthroplasty: A Systematic Review and Meta-analysis of Prospective Randomized Controlled Trials
Bibliographic record
Abstract
Background: Recent evidence on the cost-effectiveness of technology in total knee arthroplasty (TKA) demonstrated that navigated computer-assisted methods (N-TKA) is likely to be most cost-effective in the clinical setting. The aim of the current meta-analysis is to compare radiographic, clinical and functional outcomes between conventional TKA (C-TKA) and N-TKA methods. Methods: All prospective randomized controlled trials (pRCTs) comparing primary TKA performed using C-TKA and N-TKA techniques were eligible for inclusion. Radiographic outcomes included postoperative coronal, sagittal and axial component alignment. Clinical outcomes included all-cause revision and aseptic revision. Functional outcomes were analyzed when reported. A random-effects meta-analysis of all available cases was performed. This allowed for all missing data. Results: Normal coronal mechanical alignment of the tibial (p < 0.001) and femoral (p = 0.001) components was achieved more frequently with N-TKA. Normal sagittal mechanical alignment of the tibial component was achieved significantly more with N-TKA (p < 0.010). There was no difference in short-term clinical survivorship (all-cause, p = 0.649; aseptic, p = 0.79) or in functional outcomes reported between groups. There was a clinically significant reduction in the mean C-TKA operative time (87 min, σ = 16.6, 95% CI 76.4-98.8) compared N-TKA (97.6 min, σ = 16.9, 95% CI 86.2-109.1) (p = 0.17). Conclusion: Navigated TKA achieves superior radiographic alignment for femoral and tibial components in both the coronal and sagittal plane. Operative times are 10 min longer in the N-TKA group. Functional outcomes are similar between navigated and conventional groups. Clinical outcomes reported in Level I studies are limited to short-term follow-up so future prospective studies are required.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".