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Record W2952407097 · doi:10.1177/1120700019860774

Catastrophic femoral head trunnion dissociation: a case series with surface wear analysis

2019· article· en· W2952407097 on OpenAlexaff
Thomas J. Wood, Mohammad M. Alzahrani, G. Daniel G. Langohr, Matthew G. Teeter, James L. Howard, Emil H. Schemitsch, Brent A. Lanting

Bibliographic record

VenueHip International · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsHamilton Health SciencesMcMaster UniversityLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineFemoral headImplantArthroplastyComplicationSurgeryOrthodonticsDentistry

Abstract

fetched live from OpenAlex

INTRODUCTION: Modular designs enhance accuracy of limb length and offset during total hip arthroplasty. However, such interfaces can be the site of corrosion and implant failure. The purpose of this study was to review 5 cases of catastrophic femoral head trunnion dissociation to better understand clinical and implant related risk factors for this complication. METHODS: We describe 5 cases of femoral head dissociation following significant trunnion wear. Patient and implant related factors are reviewed. Surface wear patterns were assessed using electron microscopy and wear patterns were described qualitatively. RESULTS: Commonalties among 5 presented cases include male, age over 65 and increased BMI. All femoral heads were cobalt chromium with a head size of at least 36 mm or greater. 4 out of 5 had high offset. All stems were the same design. Surface wear analysis showed material removed initially on the inferomedial aspect of the trunnion where the femoral head contacted the stem. CONCLUSIONS: Femoral head dissociation secondary to failure of the trunnion is a rare complication. We show associations with older age, increased BMI and male patients with increased femoral head size and offset. Material is removed initially inferomedially and progresses to fracturing the superior aspect of the trunnion.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.267
Teacher spread0.256 · 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.

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

Citations11
Published2019
Admission routes1
Has abstractyes

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