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Record W2589830877

PATELLOFEMORAL JOINT SURFACE WEAR IN RETRIEVED FEMORAL COMPONENTS OF A SINGLE DESIGN

2018· article· en· W2589830877 on OpenAlexaff
Jacob Matz, Brent A. Lanting, James L. Howard, Matthew G. Teeter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsWestern University
Fundersnot available
KeywordsPatellofemoral jointPatellaOrthodonticsMagnificationMedicineKinematicsProfilometerBiomechanicsArticular surfaceBiomedical engineeringMaterials scienceAnatomyComputer scienceArtificial intelligenceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Introduction Anterior knee pain following total knee arthroplasty continues to be prevalent and may result from abnormal loading of the patellofemoral joint. The kinematics and biomechanics of the patellofemoral joint are complex, and trochlear design likely plays a principle role in affecting patellofemoral contact. As such, understanding the implications of trochlear design on patellofemoral contact remains important. The goal of the present study was to characterize trochlear wear of retrieved femoral components, which may help elucidate the details regarding patellofemoral kinematics and contact properties in relation to design features. Materials and Methods Retrieved femoral components featuring a single design (cobalt-chrome, posterior stabilized, cemented components with fixed bearing design) were included in the study. Components were selected based on similar time-in-vivo, age, and BMI. The trochlea of femoral components was consistently divided into six equal zones. Trochlear wear and surface damage in each zone were assessed using visual inspection under low-magnification light microscopy and light profilometry. Results Ten implants were selected and were used for the topographical analysis. The implants were selected based on time-in-vivo (33.6 months±18), BMI (40.4 kg/m2±13.2), patient age (67.9 years old±13.3) and gender (6 males, 4 females). Revision diagnosis across the implants were infection (n=6), instability (n=2), loosening (n=1), and fracture (n=1). All zones of the trochlea of retrieved femoral components showed evidence of wear on visual assessment, however, surface profilometry showed that the amount of wear in the retrieved components was not significantly different from a new, unused reference component (p>0.05). In fact, surface skeweness was higher in the new component (p=0.026). Modes of wear included scratches (100%), striations (65%), pitting (43%), and delamination (13%). Zone 1, which includes the raised lateral flange, tended to have more damage than the other zones, but this was statistically non-significant (p=0.634). No significant differences were found between the remaining trochlear zones with respect to wear based on visual assessment and light-microscopy (p=0.634) or surface profilometry (p=0.469). No significant differences were found with between proximal and distal wear (p>0.05) as well as medial and lateral trochlear wear (p>0.05). Conclusions Femoral components exhibit trochlear wear after in-vivo use. The amount of wear, however, is not substantially different from its new state and may represent early polishing. While the raised lateral flange zone trended towards greater wear than other zones, this was not statistically significant. Overall, with modern trochlear design, there was no evidence of asymmetric or abnormal loading of the trochlea. Longer term retrieval studies are required to assess patterns of femoral component wear and determine the clinical correlation of these findings.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.172
GPT teacher head0.257
Teacher spread0.085 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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