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Record W2285849451 · doi:10.1243/09544119jeim322

Strain pattern following surface replacement of the hip

2008· article· en· W2285849451 on OpenAlexaff
Muthu Ganapathi, Samuel Lewin Evans, Paul R. Roberts

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsFemurFemoral neckStrain (injury)Greater trochanterLesser TrochanterStrain gaugeAnatomyNotchingMedicineMaterials scienceSurgeryComposite materialOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

The aim is to compare the strain pattern in intact and resurfaced femurs using validated third-generation composite femurs and rosette strain gauges. The rosette strain gauges were applied to an intact and a resurfaced third-generation composite femur at three sites: the narrowest part of the lateral surface of the neck, the narrowest part of the medial surface of the neck, and the medial surface at the level of the lesser trochanter. The maximum and minimum principal strains were calculated at axial loads of 600, 800, and 1000 N. Further tests were carried out with an additional abductor load. The maximum principal strains in the resurfaced femur were approximately 50 per cent higher in the lateral surface of the neck and about 25 per cent higher in the lesser trochanteric region than in the intact femur. Inclusion of the abductor force decreased the strains in both the intact and the resurfaced femurs, particularly at the lateral surface of the femoral neck. Increased strain at the lateral surface of the femoral neck following hip resurfacing could be a cause of neck fracture, particularly if there are other predisposing factors such as notching of the femoral neck and/or abductor dysfunction. Meticulous repair of the abductors is warranted if a lateral approach is used.

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)0.001
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 score0.116
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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

Citations29
Published2008
Admission routes1
Has abstractyes

Explore more

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