Why personalized surgery is the future of hip and knee arthroplasty: a statement from the Personalized Arthroplasty Society
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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