Comparison of functional results with navigation-assisted minimally invasive and conventional techniques in bilateral total knee arthroplasty
Bibliographic record
Abstract
This study was undertaken to compare the clinical and radiological results achieved using navigation-assisted minimally invasive surgery (NA-MIS) and conventional (CON) techniques in 42 bilateral total knee arthroplasty (TKA) patients with a minimum follow-up of one year. Clinical evaluations were performed using range of motion (ROM), Hospital for Special Surgery (HSS) scores, and Western Ontario and McMaster University (WOMAC) scores (pain, functional, and total) at 3, 6 and 9 months and one year postoperatively. Patients' subjective preferences and radiological indices, including mechanical axis and coronal inclinations of the prostheses, were compared at one year postoperatively. NA-MIS TKA yielded better HSS and WOMAC total scores than CON TKA up to six months, and a better WOMAC pain score up to 9 months. However, these differences were not significant at one year postoperatively. ROM was comparable in both groups at all times, but more patients preferred the NA-MIS side to the CON side. Radiological results showed no differences in mean values between the two surgical groups, although the NA-MIS group contained fewer outliers than the CON group. In conclusion, NA-MIS TKA was associated with better clinical results up to 6 or 9 months after surgery, giving more accurate leg alignment than CON TKA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".