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<i>In vivo</i> wear measurement in a modern total knee arthroplasty with model-based radiostereometric analysis

2019· article· en· W2988769647 on OpenAlexaff
Trevor C. Gascoyne, Sara Parashin, Matthew G. Teeter, Éric Bohm, Elise Laende, Michael Dunbar, Thomas R. Turgeon

Bibliographic record

VenueThe Bone & Joint Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsNova Scotia Health AuthorityUniversity of ManitobaWestern UniversityLondon Health Sciences CentreOrthopaedic Innovation CentreConcordia Hospital
Fundersnot available
KeywordsTotal knee arthroplastyOrthodonticsArthroplastyMedicineSurgery

Abstract

fetched live from OpenAlex

Aims A retrospective study was conducted to measure short-term in vivo linear and volumetric wear of polyethylene (PE) inserts in 101 total knee arthroplasty (TKA) patients using model-based radiostereometric analysis (MBRSA). Patients and Methods Nonweightbearing supine RSA exams were performed postoperatively and at six, 12, and 24 months. Weightbearing standing RSA exams were performed on select patients at 12 and 24 months. Wear was measured both linearly (joint space) and volumetrically (digital model overlap) at each available follow-up. Precision of both methods was assessed by comparing double RSA exams. Patient age, sex, body mass index, and Oxford Knee Scores were analyzed for any association with PE wear. Results Linear wear occurred at 0.015 mm/year (supine) and 0.220 mm/year (standing). Volumetric wear occurred at 10.3 mm 3 /year (supine) and 39.3 mm 3 /year (standing). Wear occurred primarily on the medial side of the joint. Weightbearing imaging greatly improved the reliability of measurement. Clinical precision of volumetric wear was 34 mm 3 . No significant associations were found between patient demographics or function scores and measured wear. Conclusion In vivo volumetric wear of TKAs can be assessed at short-term follow-up using MBRSA. Cite this article: Bone Joint J 2019;101-B:1348–1355.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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 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
Published2019
Admission routes1
Has abstractyes

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