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Effect of head size and rotation on taper corrosion in a hip simulator

2021· article· en· W3217273202 on OpenAlexaff
Christian M. Wight, Cari Whyne, Earl R. Bogoch, Radovan Zdero, David M. F. Chapman, Douglas W. Van Citters, William R. Walsh, Emil H. Schemitsch

Bibliographic record

VenueBone & Joint Open · 2021
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWestern UniversityLondon Health Sciences CentreSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsCorrosionGaitMaterials scienceHead (geology)Femoral headStructural engineeringMetallurgyGeologySurgeryMedicineEngineeringPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Aims This study investigates head-neck taper corrosion with varying head size in a novel hip simulator instrumented to measure corrosion related electrical activity under torsional loads. Methods In all, six 28 mm and six 36 mm titanium stem-cobalt chrome head pairs with polyethylene sockets were tested in a novel instrumented hip simulator. Samples were tested using simulated gait data with incremental increasing loads to determine corrosion onset load and electrochemical activity. Half of each head size group were then cycled with simulated gait and the other half with gait compression only. Damage was measured by area and maximum linear wear depth. Results Overall, 36 mm heads had lower corrosion onset load (p = 0.009) and change in open circuit potential (OCP) during simulated gait with (p = 0.006) and without joint movement (p = 0.004). Discontinuing gait’s joint movement decreased corrosion currents (p = 0.042); however, wear testing showed no significant effect of joint movement on taper damage. In addition, 36 mm heads had greater corrosion area (p = 0.050), but no significant difference was found for maximum linear wear depth (p = 0.155). Conclusion Larger heads are more susceptible to taper corrosion; however, not due to frictional torque as hypothesized. An alternative hypothesis of taper flexural rigidity differential is proposed. Further studies are necessary to investigate the clinical significance and underlying mechanism of this finding. Cite this article: Bone Jt Open 2021;2(11):1004–1016.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.022
GPT teacher head0.315
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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