Why personalized surgery is the future of hip and knee arthroplasty: a statement from the Personalized Arthroplasty Society
Bibliographic record
Abstract
Although hip and knee joint replacements provide excellent clinical results, many patients still do not report the sensation and function of a natural joint. The perception that the joint is artificial may result from the anatomical modifications imposed by the surgical technique and the implant design. Moreover, the joint replacement material may not function similarly to human tissues. To restore native joint kinematics, function, and perception, three key elements play a role: (i) joint morphology (articular surface geometry, bony anatomy, etc.), (ii) lower limb anatomy (alignment, joint orientation), and (iii) soft tissue laxity/tension. To provide a 'forgotten joint' to most patients, it is becoming clear that personalizing joint replacement is the key solution. Performing a personalized joint replacement starts with patient selection and preoperative optimization, followed by using a surgical technique and implant design aimed at restoring the patient's native anatomy, creating optimal implant-to-bone stress transfer, restoring the joint's native articular range of motion without imposed limitations, macro- and micro-stability of the soft tissues, and a bearing whose wear resistance provides lifetime survivorship with unrestricted activities. In addition, the whole perioperative experience should follow enhanced recovery after surgery principles, favoring a rapid and complication-free recovery. As a new concept, some confusion may arise when applying these personalized surgery principles. Therefore, the Personalized Arthroplasty Society was created to help structure and accelerate the adoption of this paradigm change. This statement from the Society on personalized arthroplasty will serve as a reference that will evolve with time.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.021 | 0.042 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".