Personalized hip joint replacements using a large diameter head
Bibliographic record
Abstract
Background: Conventional total hip arthroplasty (THA) aims to restore native joint kinematics, yet complications such as dislocation, implant malpositioning, and restricted range of motion (ROM) persist. Achieving optimal functional outcomes requires precise anatomical restoration and accelerated postoperative rehabilitation to meet increasing patient expectations for activity and joint perception. Objective: This article examines the clinical application of personalized THA utilizing large diameter head (LDH) bearings—specifically ceramic-on-ceramic (CoC) and dual mobility (DM) designs—integrated with enhanced recovery after surgery (ERAS) protocols. Key Points: LDH THA, defined by femoral heads exceeding 36 mm, increases jump distance and the head-to-neck ratio, significantly reducing dislocation risk and providing supraphysiological ROM. This configuration compensates for surgical imprecision and variations in spinopelvic mobility. Clinical evidence supports the use of CoC LDH for patients with a life expectancy over 20 years due to superior wear resistance and reduced osteolysis. Conversely, DM LDH is indicated for older or higher-risk populations. LDH bearings also enhance micro-stability via increased suction forces. Implementation of ERAS protocols has been shown to reduce postoperative complications by 50% and facilitate outpatient surgery by decreasing hospital length of stay. While CoC bearings may produce audible noise, it is generally benign and does not correlate with decreased functional scores. Trunnionosis risks are mitigated through the use of ceramic femoral heads. Conclusion: The integration of LDH bearings with ERAS principles facilitates precise biomechanical reconstruction and rapid functional recovery. This combined approach optimizes implant stability and survivorship, providing a viable pathway toward achieving a forgotten joint in personalized hip reconstruction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".