Less gap imbalance with restricted kinematic alignment than with mechanically aligned total knee arthroplasty: simulations on 3-D bone models created from CT-scans
Bibliographic record
Abstract
Background and purpose - Mechanical alignment techniques for total knee arthroplasty (TKA) introduce significant anatomic alteration and secondary ligament imbalances. We propose a restricted kinematic alignment (rKA) protocol to minimize these issues and improve TKA clinical outcomes.Patients and methods - rKA tibial and femoral bone resections were simulated on 1,000 knee CT scans from a database of patients undergoing TKA. rKA was defined by the following criteria: independent tibial and femoral cuts within 5° of the bone neutral mechanical axis, with a resulting HKA within 3° of neutral. Imbalances in the extension space, flexion space at 90°, medial compartment and lateral compartment were calculated and compared with measured resection mechanical alignment (MA) results. 2 MA techniques were simulated for rotation using the surgical transepicondylar axis (TEA) and 3° to the posterior condyles (PC).Results - Extension space imbalances ≥ 3 mm occurred in 33% of TKAs with MA technique versus 8.3% with rKA (p < 0.001). Similarly, more frequent flexion space imbalance ≥ 3mm was created by MA technique (TEA 34% or 3° PC 15%) versus rKA (6.4%, p < 0.001). Using MA with TEA or PC, there were only 49% and 63% of the knees respectively with < 3 mm of imbalance throughout the extension and flexion spaces and medial and lateral compartments versus 92% using rKA (p < 0.001).Interpretation - significantly fewer imbalances are created using rKA versus MA for TKA. rKA may be the best compromise, by helping the surgeon to preserve native knee ligament balance during TKA and avoid residual instability, whilst keeping the lower limb alignment within a safe range.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".