Short- to mid-term outcomes of computer navigation assisted total knee arthroplasty using adjusted mechanical alignment compared to mechanical alignment
Bibliographic record
Abstract
Abstract Purpose To evaluate efficacy of navigation-assisted total knee arthroplasty (TKA) achieved using adjusted mechanical alignment (aMA) and mechanical alignment (MA) technique. Methods The authors performed a retrospective study enrolled a single-center series of patients who underwent navigation-assisted TKA with aMA (n = 77) and with MA (n = 61) technique. The demographic data was recorded. Functional scores including Hospital for Special Surgery score, Western Ontario and McMaster Universities Osteoarthritis Index score and Forgotten Joint Score-12 were evaluated. In addition, the parameter of resection and soft tissue balance as well as radiographic evaluation was measured and compared between groups. Results The HSS score at 1-month and 6-months postoperatively were significant higher using aMA compared to MA. The postoperative coronal alignment was made with a mean of 1.11° more varus/valgus in the aMA group compared to MA. The femoral prosthesis was positioned in a mean of 2.29° more varus/valgus using aMA compared to MA. The medial extension gap was significantly tighter in the MA group. In addition, the femoral prosthesis in the aMA group was positioned in a mean of 0.77° more external rotation than the MA group. The lateral flexion gap was wider in the aMA group with a mean of 0.71 mm more laxity. Conclusions Both aMA and MA technique in TKA obtained good clinical outcomes. Notably, aMA-TKA grant superior functional scores at 1-month and 6-months follow-up, might due to the preservation of mild constitutional frontal deformity with less release of soft tissue and a biomimetic wider lateral flexion gap was remained.
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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 | 0.001 | 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".