Bone resection for mechanically aligned total knee arthroplasty creates frequent gap modifications and imbalances
Bibliographic record
Abstract
PURPOSE: The objective of this study was to calculate bone resection thicknesses and resulting gap sizes, simulating a measured resection mechanical alignment (MA) technique for total knee arthroplasty (TKA). METHODS: MA bone resections were simulated on 1000 consecutive lower limb CT scans from patients undergoing TKA. Femoral rotation was aligned with either the surgical trans-epicondylar axis (TEA) or with 3° of external rotation to the posterior condyles (PC). Imbalances in the extension space, flexion space, medial compartment and lateral compartment were calculated. RESULTS: Extension space imbalances (≥ 3 mm) occurred in 25% of varus and 54% of valgus knees and severe imbalances (≥ 5 mm) were present in up to 8% of varus and 19% of valgus knees. Higher flexion space imbalance rates were created with TEA versus PC (p < 0.001). Using TEA, only 49% of varus and 18% of valgus knees had < 3 mm of imbalance throughout the extension and flexion spaces, and medial and lateral compartments. CONCLUSION: A systematic use of the simulated measured resection MA technique for TKA leads to many cases with imbalance. Some imbalances may not be correctable surgically and may result in TKA instability. Modified versions of the MA technique or other alignment methods that better reproduce knee anatomies should be explored. LEVEL OF EVIDENCE: 2.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".