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Record W3013036905

Femoral head reposition first, device assemble later: If reduction is impossible when using MUTARS®

2020· article· en· W3013036905 on OpenAlexaboutno aff
Gyu Min Kong

Bibliographic record

Venue˜The œJournal of Orthopaedics Trauma Surgery and Related Research · 2020
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticMedicineReduction (mathematics)Femoral headSurgerySoft tissueImplantFemurDislocationArthroplasty
DOInot available

Abstract

fetched live from OpenAlex

This Dislocation occurs in about 1% after a total hip arthroplasty, but the frequency is much higher after revision surgery. To prevent dislocation, a procedure using a larger femoral head has been recommended, and dual mobility femoral head has been introduced to broaden its indications. However, it is very difficult to reduce the dual mobility femoral head to the acetabular component with contracture in the soft tissue around the joint. A 72-year-old male patient developed a periprosthetic fracture (Vancouver type B3) in his femur and underwent revision surgery using MUTARS®. Three years later, periprosthetic joint infection developed and 2 stage revision was performed. Dislocation of the artificial joint two months after the revision was happen and manual reduction was performed, but dislocation occurs again. New revision was undergone using dual mobility bearing. During surgery, the soft tissue around the hip joint was too tight to reduce, the problem could be overcome by first repositioning the femoral head and then assembling the diaphyseal portion of the implant.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.007

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 head0.117
GPT teacher head0.350
Teacher spread0.234 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venue˜The œJournal of Orthopaedics Trauma Surgery and Related Research→Same topicOrthopaedic implants and arthroplasty→French-language works237,207→