Kinematically Versus Mechanically Aligned Total Knee Arthroplasty
Bibliographic record
Abstract
The purpose of this study was to compare 2 alignment methods for total knee arthroplasty (TKA): kinematic alignment with the use of patient-specific guides and mechanical alignment with conventional instruments. A randomized, controlled trial of 41 kinematically aligned and 41 mechanically aligned patients was conducted with the patient, radiographic evaluator, and clinical evaluator blinded to the alignment technique. Radiographic measurements were made from long-leg computer tomography scanograms. Clinical outcome scores and motion were measured preoperatively and 6 months postoperatively. The hip-knee-ankle angle (0.3° difference; P=.693) and anatomic angle of the knee (0.8° difference; P=.131) were similar for both groups. In the kinematically aligned group, the angle of the femoral component was 2.4° more valgus (P<.000) and the angle of the tibial component was 2.3° more varus (P<.000) than the mechanically aligned group. At 6 months postoperatively, the Western Ontario and McMaster Universities Osteoarthritis Index score was 16 points better (P<.000), Oxford Score was 7 points better (P=.001), combined Knee Society Score was 25 points better (P=.001), and flexion was 5.0° greater (P=.043) in the kinematically aligned group than in the mechanically aligned group.Our findings suggest that the risk of early failure related to limb or knee alignment should be similar in kinematic and mechanically aligned TKA. More anatomic alignment of the implant was associated with better flexion and better clinical outcome scores in the kinematically aligned group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".