No differences in long‐term clinical outcomes and survival rate of navigation‐assisted versus conventional primary mobile‐bearing total knee arthroplasty: A minimum 10‐year follow‐up
Bibliographic record
Abstract
PURPOSE: This study aimed to compare long-term clinical and radiographic outcomes and survival rates between navigation-assisted (NAV) total knee arthroplasty (TKA) and conventional (CON) TKA using a mobile-bearing insert. METHODS: From May 2008 to December 2009, 45 and 63 mobile-bearing TKA patients were enroled in the CON- and NAV-TKA groups with 146.8 months follow-up, respectively. Clinical outcomes (Western Ontario and McMaster University Osteoarthritis Index and Knee Society Scores), radiographic outcomes (hip-knee-ankle [HKA], lateral distal femoral, medial proximal tibial, γ, and δ angles), and survivorship were compared between both groups. RESULTS: The number of HKA angle outliers (more than 3 degrees or less than -3 degree) was significantly lower in the NAV-TKA group (24.4% vs. 9.5%, p = 0.036) than in the CON-TKA group. However, long-term clinical outcomes were similar between both groups. The cumulative survival rate (best-case scenario) was 98.3% in the CON-TKA group and 97.5% in the NAV-TKA group, with no significant difference between the groups (p = 0.883). CONCLUSION: Long-term clinical outcomes and survival rates were similar between the two groups despite fewer outliers of postoperative lower-limb alignment in the NAV-TKA group. Excellent survival rates were observed in both groups using mobile-bearing inserts. LEVEL OF EVIDENCE: Level IV, case series.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".