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The treatment of periprosthetic femoral fractures after total hip arthroplasty

2019· article· en· W3032560946 on OpenAlexaboutno aff
Pengde Kang, Donghai Li

Bibliographic record

VenueZhonghua guke zazhi · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticMedicineProsthesisSurgeryFemoral fractureArthroplastyFemur

Abstract

fetched live from OpenAlex

Periprosthetic femoral fracture (PFF) is one of severe complications after total hip arthroplasty (THA). As the number of patients receiving THA increased recently, the incidence of PFFs also increased dramatically. There are a number of risk factors for PFFs, such as age, sex, falling and prosthesis loosening. The Vancouver classification system is the most commonly used classification method for PFFs. According to the fracture location, PFFs can be divided into type A intertrochanteric fracture, type B fracture around the stem and type C fracture beyond the stem. The Vancouver type B PFF is further subdivided into type B1 with a well-fixed prosthesis, type B2 with a loose prosthesis but with adequate bone stock, and type B3 with a loose prosthesis and poor proximal bone stock simultaneously. Currently, there are some controversies in treating PFFs, mainly including whether the stem is fixed or not, whether the prosthesis needs to be revised, the selection of the stem, the reconstruction of bone defects, and the methods of fracture fixation. We searched literatures related to PFFs after THA. The incidence, risk factors, classification methods, treatment principles and strategies of PFFs were summarized in the present study. Based on our long-term clinical experience, we evaluated the advantages and disadvantages of each treatment method and provided considerations for the clinical research and selection in treating PFFs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
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)0.0010.001

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 head0.249
Teacher spread0.241 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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