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Record W2407434876 · doi:10.1097/blo.0b013e3181560c6c

Management of Periacetabular Bone Loss in Revision Hip Arthroplasty

2007· article· en· W2407434876 on OpenAlexaff
Petros J. Boscainos, Catherine Kellett, Anthony C. Maury, David Backstein, Allan E. Gross

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto East General HospitalUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineBone graftingTrabecular boneArthroplastySurgeryFixation (population genetics)OsteoporosisPopulationPathology

Abstract

fetched live from OpenAlex

The goals of acetabular revision surgery are to restore the anatomy and achieve stable fixation for the new acetabular component. The existing bone stock and the type of defect are determining factors in the surgical decision making. When necessary, and especially in younger patients, attempts should be made to restore the bone stock by grafting. The advent of modern reconstruction options, like the trabecular metal revision system and the cup-cage construct, provide more options in addressing the management of severe defects. Trabecular metal has a porosity similar to bone and provides an environment more favorable to bone graft remodeling than conventional metals. We present an overview of our experience and current approach to acetabular revision. In addition, we report our preliminary results with trabecular metal cups and trabecular metal cup-cage constructs used in conjunction with bone graft for addressing major bone defects.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.062
GPT teacher head0.411
Teacher spread0.349 · 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

Citations61
Published2007
Admission routes1
Has abstractyes

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