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The treatment of periprosthetic femur fracture after hip arthroplasty

2013· article· en· W3031998482 on OpenAlexaboutno aff
Yan Zi-gui, MA Chun-qing, Yonghan Cha, Xiaodong Sun

Bibliographic record

VenueZhongguo jiceng yiyao · 2013
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriprostheticProsthesisSurgeryFemurArthroplastyFixation (population genetics)Femur fractureInternal fixationHip arthroplastyFemoral fractureDentistry

Abstract

fetched live from OpenAlex

Objective To explore the treatment methods of periprosthetic femur fracture after hip arthroplasty.Methods 9 patiens with periprosthetic femur fracture after hip arthroplasty were selected.According to Vancouver classification,there were 1 case in A type,5 cases in B1 type,2 cases in B2 type,1 case in C type.One case were treated by nonoperative method and the other 8 cases were treated by operative method,including 5 cases treated by memory alloy embracing fixator and internal fixation,3 cases treated by long stem prosthetic replacement and iliac bone graft.Results 8 cases were followed up for 8 to 21 months,average 14.2 months.All fractures were united well with good alignment and internal fixation failure except one prosthesis loosing was observed.Conclusion For periprosthetic femur fracture after hip arthroplasty,Vancouver classification methods include the location and stability of the fracture,prosthesis loosening,and the femur in bone mass is importance to the clinical treatment.As to A type fracture,prosthesis is stable,and the conservative treatment can be choosed.As to B1 and B2 type fracture,the aggressive surgical treatment can be choosed based on patients' general condition.If prosthesis loosening after artificial hip arthroplasty,the patients should treated with long stem prosthetic replacement. Key words: Arthroplasty, replacement, hip;  Prosthesis;  Fractures, bone;  Treatment

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 teacher head, not a consensus.

Study designOther design
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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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