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Record W2094986600 · doi:10.1115/1.2720907

The Effect of Cross-Sectional Stem Shape on the Torsional Stability of Cemented Implant Components

2006· article· en· W2094986600 on OpenAlexaff
Angela E. Kedgley, Sarah E. Takaki, Pencilla Lang, Cynthia E. Dunning

Bibliographic record

VenueJournal of Biomechanical Engineering · 2006
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWestern University
Fundersnot available
KeywordsTorsion (gastropod)Materials scienceImplantComposite materialTorqueRotation (mathematics)Initial stabilityGeometryStructural engineeringMathematicsEngineeringPhysicsAnatomySurgery

Abstract

fetched live from OpenAlex

Stability of a cemented implant, once the stem-cement interface has debonded, is reliant upon stem geometry and surface finish. There are relatively few studies addressing the effect of cross-sectional stem shape on cemented implant fixation. The purpose of this investigation was to compare the torsional stability of five different stem cross-sectional shapes-circular, oval, triangular, rectangular with rounded edges, and rectangular with sharp edges-under monotonically increasing and cyclic loading conditions. Seven samples of each stem geometry were tested. Stems were potted in bone cement and loaded to 5 deg of rotation. For monotonic loading, torque was applied at a constant rate of 2.5 deg/min. For cyclic loading, a sine wave torque pattern was applied, with a maximum magnitude that began at 4.5 Nm for 1500 cycles and then increased by 2.25 Nm every 1500 cycles until 5 deg of rotation. The rectangular stem with the sharp edges always provided the greatest resistance to torque, followed by the rectangular with rounded edges, triangular, oval, and circular. These results, including the effects of sharp corners, may differ for modes of loading other than torsion. These experimental results support the findings of earlier finite element models, indicating stem shape has a significant effect on resistance to torsional loading.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.018
GPT teacher head0.257
Teacher spread0.238 · 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 designBench or experimental
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

Citations17
Published2006
Admission routes1
Has abstractyes

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