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Record W4400598194 · doi:10.5312/wjo.v15.i7.668

Bone graft incorporation failure with inappropriate limb load transfer can lead to aseptic acetabular loosening of metal-on-metal prosthesis: A case report

2024· article· en· W4400598194 on OpenAlexaboutno aff
Rafał Stanisław Domagalski, Bogdan Dugiełło, Sonia Rokicka, Szymon Czech, Rafał Skowroński, Dominika Rokicka, Marta Wróbel, Krzysztof Strojek, Tomasz Stołtny

Bibliographic record

VenueWorld Journal of Orthopedics · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryStress shieldingProsthesisImplantArthroplastyWeight-bearingAcetabulumHip surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Aseptic acetabular loosening can result from various factors that can be categorized into groups: patient-related, surgeon-related and implant-related. We present a case of a 63-year-old patient who at first underwent a total hip arthroplasty (THA) using a metal-on-metal bearing due to hip arthrosis. Follow-up visits revealed no complications after the procedure. Two years after the THA, acetabular component loosening occurred due to subsequent trauma of the opposite hip, necessitating a revision THA using a ceramic-on-ceramic bearing. CASE SUMMARY: We aim to illustrate a rare case where the primary reason for undergoing THA revision was not only incomplete bone graft incorporation but also improper limb load distribution. Following the revision arthroplasty, a 9-year follow-up visit revealed improvements in all evaluation measures on questionnaire compared to the state before surgery: Harris Hip Score (before surgery: 15; after surgery: 95), Western Ontario and McMaster Universities Arthritis Index (before surgery: 96; after surgery: 0), and Visual Analogue Scale (before surgery: 10; after surgery: 1). CONCLUSION: Opposite-hip trauma caused a weight transfer to the limb after a THA procedure. This process led to a stress shielding effect, resulting in acetabular component loosening.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 designCase report
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

Citations0
Published2024
Admission routes1
Has abstractyes

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