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Record W3212207438 · doi:10.1002/9781119413936.ch56

Periprosthetic Fractures: Knee

2021· other· en· W3212207438 on OpenAlexaff
Jesse Wolfstadt, Aaron Nauth

Bibliographic record

VenueEvidence-Based Orthopedics · 2021
Typeother
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsPeriprostheticMedicineIntramedullary rodSurgeryFemurInternal fixationReduction (mathematics)Arthroplasty

Abstract

fetched live from OpenAlex

This chapter presents a case scenario of a 78-year-old female patient with a previously well-functioning right total knee arthroplasty (TKA) who presents to the Emergency Department after suffering a fall from standing height. The management of periprosthetic distal femur fractures can be challenging and is often complicated by poor bone quality or bone stock. Stable components with adequate bone stock are often treated with open reduction and internal fixation (ORIF) with either retrograde intramedullary nailing (RIMN) or periarticular locked plating. Modern implants, including RIMN and locked plating, have been used to treat periprosthetic distal femur fractures. A major concern with ORIF of periprosthetic distal femur fractures is the ability to achieve stable fixation in a short, osteoporotic fragment of distal bone. Extreme distal periprosthetic femur fractures can be adequately managed with ORIF in the setting of a stable femoral component. The chapter provides recommendations for implementing evidence-based practice in the clinical setting.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0240.006

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 head0.036
GPT teacher head0.310
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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