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PERIPROSTHETIC FRACTURES AFTER JOINT REPLACEMENT: A UNIFIED CLASSIFICATION SYSTEM

2018· article· en· W2800579956 on OpenAlexaffabout
Lisa C. Howard, Clivе P. Duncan

Bibliographic record

VenueTraumatology and Orthopedics of Russia · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeriprostheticArthroplastyMedicineGold standard (test)ImplantJoint (building)Joint replacementJoint arthroplastyJoint infectionsSurgeryComputer scienceRadiologyEngineering

Abstract

fetched live from OpenAlex

Periprosthetic fracture associated with joint replacement is a common reason for revision arthroplasty and is increasing. Establishing universal principles of management is essential for good outcomes and a classification system that not only classifies, but offers these principles, is critical to achieve this. The Vancouver Classification System (VCS) for periprosthetic fractures involving total hip arthroplasty is validated across North America and Europe. It does not, however, consider other periprosthetic fractures in different joints. The Unified Classification System (UCS) was developed to incorporate the classification and treatment principles of all periprosthetic fractures in any anatomic location. The system is based on the simple mnemonic “ABCDEF” which corresponds to fractures characterized by the following anatomic descriptors: 1) apophyseal; 2) bed of the implant; 3) clear of the implant; 4) dividing the bone between two arthroplasties; 5) each of two bones supporting one arthroplasty; 6) facing and articulating with an implant. Initial validation for the UCS shows substantial and near-perfect inter and intra-observer agreement. Given this performance, it has the potential to evolve into the gold standard classification system for periprosthetic fractures in any joint that they occur.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.418

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.275
Teacher spread0.255 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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