MétaCan
Menu
Back to cohort
Record W2137618515 · doi:10.4055/cios.2011.3.2.101

Treatment of Periprosthetic Femoral Fractures in Hip Arthroplasty

2011· article· en· W2137618515 on OpenAlexaboutno aff
Sung Ki Park, Young Gun Kim, Shin‐Yoon Kim

Bibliographic record

VenueClinics in Orthopedic Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriprostheticNonunionSurgeryRadiological weaponHarris Hip ScoreSubsidenceArthroplastyTotal hip arthroplasty

Abstract

fetched live from OpenAlex

BACKGROUND: We analyzed the radiological and clinical results of our study subjects according to the management algorithm of the Vancouver classification system for the treatment of periprosthetic femoral fractures in hip arthroplasty. METHODS: We retrospectively reviewed 18 hips with postoperative periprosthetic femoral fractures. The average follow-up was 49 months. The fracture type was determined based on the Vancouver classification system. The management algorithm of the Vancouver classification system was generally applied, but it was modified in some cases according to the surgeon's decision. At the final follow-up, we assessed the radiological results using Beals and Tower's criteria. The functional results were also evaluated by calculating the Harris hip scores. RESULTS: Seventeen of 18 cases (94.4%) achieved primary union at an average of 25.5 weeks. The mean Harris hip score was 92. There was 1 case of nonunion, which was a type C fracture after cemented total hip arthroplasty, and this required a strut allograft. Subsidence was noted in 1 case, but the fracture was united despite the subsidence. There was no other complication. CONCLUSIONS: Although we somewhat veered out of the management algorithm of the Vancouver classification system, the customized treatment, with considering the stability of the femoral stem and the configuration of the fracture, showed favorable overall results.

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)0.001
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 score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.073
GPT teacher head0.325
Teacher spread0.252 · 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

Citations38
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueClinics in Orthopedic SurgerySame topicOrthopaedic implants and arthroplastyFrench-language works237,207