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Record W4242727560 · doi:10.5301/jabb.2012.9336

The effect of stem curvature on torsional stability of a generalized cemented joint replacement system

2012· article· en· W4242727560 on OpenAlexaff
Yara K. Hosein, Meghan P. Clynick, Stewart McLachlin, Graham J.W. King, Cynthia E. Dunning

Bibliographic record

VenueJournal of Applied Biomaterials & Functional Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsCurvatureTorqueMaterials scienceImplantRotation (mathematics)Initial stabilityOrthodonticsMathematicsSurgeryMedicinePhysicsGeometryThermodynamics

Abstract

fetched live from OpenAlex

PurposeImplant loosening is a common complication that compromises the stability of joint replacement systems. Stem geometry is particularly influential in the stability of cemented implants, both before and after debonding occurs at the stem-cement interface. There are few studies assessing the effect of stem longitudinal curvature as a geometric factor in cemented implant stability. The purpose of this study was to compare the torsional stability of four generalized cemented implant stems (i.e., non-specific to joint), with varying degrees of longitudinal curvatures - zero, two, four, and six degrees. MethodsTwelve specimens of each curvature angle were potted to a depth of 20 mm using bone cement, given 24 hours to cure, and then tested in a materials testing machine. Torque was applied to the stems under monotonic loading at a rate of 2.5 degrees/min, until five degrees of rotation had occurred. ResultsThere were no differences in torsional stability among the four stem curvature angles, when the magnitudes of peak torque (P=.72; 1-β = 0.13), rotation of the stem at peak torque (P=0.23; 1-β = 0.38) and work required for five degrees of stem rotation (P=.58; 1-β = 0.07) were compared. ConclusionsThe findings from this study demonstrate that for short stems, stem curvature angles up to six degrees does not improve torsional stability when compared to the straight stem design.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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