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Record W2971687071 · doi:10.1302/2058-5241.4.180045

Dual mobility total hip arthroplasty: should everyone get one?

2019· review· en· W2971687071 on OpenAlexaff
William G. Blakeney, Jean-Alain Epinette, Pascal‐André Vendittoli

Bibliographic record

VenueEFORT Open Reviews · 2019
Typereview
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsDual (grammatical number)Total hip arthroplastyArthroplastyHip arthroplastyMedicineSurgeryArt

Abstract

fetched live from OpenAlex

Abstract Hip instability following total hip arthroplasty (THA) remains a major challenge and is one of the main causes of revision surgery. Dual mobility (DM) implants have been introduced to try to overcome this problem. The DM design consists of a small femoral head captive and mobile within a polyethylene liner. Numerous studies have shown that DM implants reduce the rate of dislocation compared to fixed-bearing inserts. Early designs for DM implants had problems with wear and intra-prosthetic dislocations, so their use was restricted to limited indications. The results of the latest generation of DM prostheses demonstrate that these problems have been overcome. Given the results of these studies presented in this review, surgeons may now consider DM THA for a wider patient selection. Cite this article: EFORT Open Rev 2019;4:541-547. DOI: 10.1302/2058-5241.4.180045

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.209
GPT teacher head0.405
Teacher spread0.196 · 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 designNot applicable
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

Citations75
Published2019
Admission routes1
Has abstractyes

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