Long-term bone remodelling around ‘legendary’ cementless femoral stems
Bibliographic record
Abstract
Abstract Bone remodelling around a stem is an unavoidable long-term physiological process highly related to implant design. For some predisposed patients, it can lead to periprosthetic bone loss secondary to severe stress-shielding, which is thought to be detrimental by contributing to late loosening, late periprosthetic fracture, and thus rendering revision surgery more complicated. However, these concerns remain theoretical, since late loosening has yet to be documented among bone ingrowth cementless stems demonstrating periprosthetic bone loss associated with stress-shielding. Because none of the stems replicate the physiological load pattern on the proximal femur, each stem design is associated with a specific load pattern leading to specific adaptive periprosthetic bone remodelling. In their daily practice, orthopaedic surgeons need to differentiate physiological long-term bone remodelling patterns from pathological conditions such as loosening, sepsis or osteolysis. To aid in that process, we decided to clarify the behaviour of the five most used femoral stems. In order to provide translational knowledge, we decided to gather the designers’ and experts’ knowledge and experience related to the design rationale and the long-term bone remodelling of the following femoral stems we deemed ‘legendary’ and still commonly used: Corail (Depuy); Taperloc (Biomet); AML (Depuy); Alloclassic (Zimmer); and CLS-Spotorno (Zimmer). Cite this article: EFORT Open Rev 2018;3:45-57. DOI: 10.1302/2058-5241.3.170024
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".