Overview of the different personalized total knee arthroplasty with robotic assistance, how choosing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current limitations in total knee arthroplasty (TKA) function and patient satisfaction stimulated us to question our practice. Our understanding of knee anatomy and biomechanics has evolved over recent years as we now consider that a more personalized joint reconstruction may be a better-targeted goal for TKA. Implant design and surgical techniques must be advanced to better reproduce the anatomy and kinematics of native knees and ultimately provide a forgotten joint. The availability of precision tools as robotic assistance surgery can help us recreate patient anatomy and ensure components are not implanted in a position that may compromise long-term outcomes. Robotic-assisted surgery is gaining in popularity and may be the future of orthopedic surgery. However, moving away from the concept of neutrally aligning every TKA dogma opens the door to new techniques emergence based on opinion and experience and leads to a certain amount of uncertainty among knee surgeons. Hence, it is important to clearly describe each technique and analyze their potential impacts and benefits. Personalized TKA techniques may be classified into 2 main families: unrestricted or restricted component orientation. In the restricted group, some will aim to reproduce native ligament laxity versus aiming for ligament isometry. When outside of their boundaries, all restricted techniques will induce anatomical changes. Similarly, most native knee having asymmetric ligaments laxity between compartments and within the same compartment during the arc of flexion; aiming for ligament isometry induces bony anatomy changes. In the current paper, we will summarize and discuss the impacts of the different robotic personalized alignment techniques, including kinematic alignment (KA), restricted kinematic alignment (rKA), inverse kinematic alignment (iKA), and functional alignment (FA). With every surgical technique, there are limitations and shortcomings. As our implants are still far from the native knee, it is primordial to understand the impacts and benefits of each technique. Mid to long data will help us in defining the new standards.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it