Comparative Analysis of Diaphyseal versus Metaphyseal tracker Placement in Imageless Navigation Systems for Total Knee Arthroplasty
Bibliographic record
Abstract
Abstract This work was designed to compare the intraoperative parameters and clinical and radiologic outcomes of total knee arthroplasty (TKA) during a minimum follow-up period of 2 years and to discuss the pros and cons of two different tracker placement (diaphyseal and metaphyseal) navigation systems. The null hypothesis was that there would be no clinical or radiologic difference between the two different systems. Primary TKA was performed in a total of 100 knees using the two different image-free navigation systems (group 1: diaphyseal tracker placement and group 2: metaphyseal tracker placement) with the strict gap balancing technique. Symptom severity was assessed at preoperative and at 3, 6, 12, and 24 months after surgery using the Knee Society Score (KSS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. Possible adverse issues (major and minor) associated with TKA procedure were observed. Careful assessments were also made of the screw insertion site for infection, stress fractures, and any other related adverse effects. The follow-up periods for groups 1 and 2 were 38 ± 8 months and 38 ± 7 months, respectively. The minimum follow-up period was 24 months. The mechanical alignment improved to 0.1 (valgus) ± 2.2 (group 1) and 0.2 (valgus) ± 2.1 (group 2). There were no radiologic differences between the groups (p > 0.05). In both groups, the KSS and WOMAC improved from before surgery to 24 months after surgery (p < 0.0001). However, the total operation time was 50 ± 5 minutes for group 1, compared to 65 ± 13 minutes for group 2 (p < 0.0001). The metaphyseal tracker navigation system resulted in increased operation time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".