Assessing the performances of collision driven numerically-simulated implantation in elbow replacement surgery
Bibliographic record
Abstract
Total elbow arthroplasty is a common surgical procedure used to replace diseased joints with an implant attempting to restore at least partially the lost functionality of the articulation. Given the relative paucity of studies attempting to simulate implant kinematics during insertion motions, the primary objective of the present study was to assess the feasibility and performances of conventional numerical computer-aided engineering (CAE) techniques in this biomechanical context. The results obtained revealed that while both CAE-driven and experimental navigated implantation techniques will yield comparable FE axis misalignment errors, the numerically-simulated approaches seem to be capable of providing more insight on the motion dynamics/kinematics due to their inherent level of maturity. Based on this, it was concluded that numerically-simulated techniques offer less invasive and more comprehensive means for implant motion control and visualisation, and therefore they should be further perfected for implant design as well as preoperative virtual surgery applications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".