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Measurements of Surface and Subsurface Damage in Retrieved Polyethylene Tibial Inserts of a Contemporary Design

2012· article· en· W2079781517 on OpenAlexafffund
Matthew G. Teeter, Jan-M. Brandt, Douglas D.R. Naudie, Éric Bohm, Richard W. McCalden, David W. Holdsworth

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2012
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsRobarts Clinical TrialsWestern UniversityConcordia HospitalLondon Health Sciences Centre
FundersCanadian Institutes of Health Research
KeywordsCreepArticular surfaceInsert (composites)Materials scienceStandard deviationAbsolute deviationPolyethyleneTotal knee replacementBiomedical engineeringNuclear medicineComposite materialMedicineGeologySurgeryMathematics

Abstract

fetched live from OpenAlex

In the present study, surface and subsurface damage due to wear and creep in retrieved tibial inserts from the Genesis II total knee replacement (Smith & Nephew, Memphis, TN) are quantified. The utility of a number of recently validated micro-computed tomography (micro-CT) techniques for use in retrieval studies are also demonstrated. Sixteen inserts retrieved from patients after an implantation time from 0.5 to 86 months were examined. The inserts were scanned using micro-CT, and the three-dimensional surface deviations (corresponding to wear and creep) between the retrieved inserts and a reference geometry were determined. The subsurface of the inserts was also examined. Deviations within damage features were measured, and a surface deviation rate (mm/ year) was calculated from the length of implantation. No subsurface fatigue damage was found. The mean deviation within the most damaged regions of the articular surface was 0.115 ± 0.064 mm medially and 0.099 ± 0.061 mm laterally (p = 0.20). The mean articular deviation rate for inserts in vivo for more than 1 year was 0.049 mm/year and was reduced to 0.026 mm/year in inserts implanted for more than 4 years. Wear and creep of the Genesis II PE insert was comparable to reported values in other total knee replacements.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 integrity0.0000.001
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 head0.056
GPT teacher head0.324
Teacher spread0.268 · 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 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

Citations7
Published2012
Admission routes2
Has abstractyes

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