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Record W2141375392 · doi:10.4055/jkoa.2007.42.2.147

Treatment of Periprosthetic Femoral Fracture according to the Vancouver Classification

2007· article· en· W2141375392 on OpenAlexaboutno aff
Il-Yong Choi, Duk-Moon Jung, Seoung-Pyo Seo, Young-Ho Kim

Bibliographic record

VenueThe Journal of the Korean Orthopaedic Association · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticFemoral fractureFracture (geology)MedicineOrthodonticsGeologySurgeryForensic engineeringEngineeringFemurArthroplastyGeotechnical engineering

Abstract

fetched live from OpenAlex

Resul ts : The overall incidence of postoperative periprosthetic femoral fracture was 0.91%. The frequency of the fracture types in decreasing order was B1, B2, B3, C, AG and AL. The treatment outcomes according to the Vancouver guidelines were excellent in 27 hips, good in 5 hips and poor in 3 hips. Suspicious risk factors of periprosthetic fractures were found in 6 hips (osteoporosis in 4 hips, osteolysis in 1 hip and loosening of femoral stem in 1 hip). C omplications related to the treat- ment included a bony defect in 1 hip and an infection with non-union in 1 hip. The complications related to treatment for an implant were loosening in 2 hips and subsidence of stem in 1 hip. Concl u si o n: In order to obtain favorable results, in addition to following the V ancouver treatment guideline, consideration should be made to the basic principles such as the stability of the fractures, the stability of the implant and restoration of the bone stock.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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
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
Published2007
Admission routes1
Has abstractyes

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