Personalized total knee arthroplasty in patients with extra-articular deformities
Bibliographic record
Abstract
Over the years, with a better understanding of knee anatomy and biomechanics, superior implant designs, advanced surgical techniques, and the availability of precision tools such as robotics and navigation, a more personalized approach to total knee arthroplasty (TKA) has emerged. In the presence of extra-articular deformities, performing personalized TKA can be more challenging and specific considerations are required, since one has to deal with an acquired pathological anatomy. Performing personalized TKA surgery in patients with extra-articular deformities, the surgeon can: (1) resurface the joint, omitting the extra-articular deformity; (2) partially compensate the extra-articular deformity with intra-articular correction (hybrid technique), or (3) correct the extra-articular deformity combined with a joint resurfacing TKA (single stage or two-stage procedure). Omitting the acquired lower limb malalignment by resurfacing the knee has the advantages of respecting the joint surface anatomy and preserving soft tissue laxities. On the other hand, it maintains pathological joint load and lower limb kinematics with potentially detrimental outcomes. The hybrid technique can be performed in most cases. It circumvents complications associated with osteotomies and brings lower limb axes closer to native alignment. On the other hand, it creates some intra-articular imbalances, which may require soft tissue releases and/or constrained implants. Correcting the extra-articular deformity (through an osteotomy) in conjunction with joint resurfacing TKA represents the only true kinematic alignment technique, as it aims to reproduce native knee laxity and overall lower limb axis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".