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Record W4236862957 · doi:10.1002/9781119413936.ch22

Highly Crosslinked Polyethylene in Total Hip Arthroplasty

2021· other· en· W4236862957 on OpenAlexaff
Glen Richardson, Michael Dunbar

Bibliographic record

VenueEvidence-Based Orthopedics · 2021
Typeother
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOsteolysisPolyethyleneTotal hip arthroplastyOrthopedic surgeryMedicineArthroplastyDentistryUltra-high-molecular-weight polyethyleneImplantSurgeryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This chapter presents a case scenario of a 55-year-old active female who presents to an orthopedic surgeon with advanced osteoarthritis in her right hip. Conservative treatment options have been exhausted and she is scheduled for a total hip arthroplasty (THA) with the use of ultra-high-molecular-weight polyethylene (UHMWPE). The purpose of highly crosslinked polyethylene (HCLPE) is to improve the longevity of THA by decreasing the wear rate of the bearing used during THA. Many of the studies are randomized controlled trials to demonstrate the improvement in wear rates with HCLPE as compared to regular polyethylene. HCLPE results in a significant reduction in polyethylene wear in vivo compared with regularly UHMWPE. There has been concern that the smaller wear particles of THA will lead to an increased risk of osteolysis compared to UHMWPE. The chapter also 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.037
GPT teacher head0.296
Teacher spread0.259 · 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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