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Record W2789401185 · doi:10.1302/2058-5241.3.170024

Long-term bone remodelling around ‘legendary’ cementless femoral stems

2018· article· en· W2789401185 on OpenAlexaff
Charles Rivière, Guido Grappiolo, Charles A. Engh, Jean-Pierre Vidalain, Antonia-F. Chen, Nicolas Boehler, Jihad Matta, Pascal‐André Vendittoli

Bibliographic record

VenueEFORT Open Reviews · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsStress shieldingPeriprostheticMedicineBone remodelingOrthopedic surgeryFemurOsteolysisSurgeryDentistryArthroplastyImplantInternal medicine

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.112
GPT teacher head0.372
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations61
Published2018
Admission routes1
Has abstractyes

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