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Record W2557283409

TKR WEAR MEASUREMENT IN 72 PATIENTS USING RADIOSTEREOMETRIC ANALYSIS

2018· article· en· W2557283409 on OpenAlexaffabout
Trevor C. Gascoyne, Sara Parashin, Thomas R. Turgeon, Éric Bohm, Elise Laende, Michael Dunbar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsCanadian Orthopaedic Trauma Society
Fundersnot available
KeywordsInsert (composites)MedicineImplantOrthodonticsRadiographyComputer scienceSurgeryEngineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Articulation of the polyethylene (PE) insert between the metal femoral and tibial components in total knee replacements (TKR) results in wear of the insert which can necessitate revision surgery. Continuous PE advancements have improved wear resistance and durability increasing implant longevity. Keeping up with these material advancements, this study utilises model-based radiostereometric analysis (mbRSA) as a tool to measure in vivo short-term linear PE wear to thus predict long-term wear of the insert. Radiographic data was collected from the QEII Health Sciences Centre in Halifax, NS. Data consisted of follow-up RSA examinations at post-operative, six-, 12-, and 24-month time periods for 72 patients who received a TKR. Implanted in all patients were Stryker Triathlon TKRs with a fixed, conventional PE bearing of either a cruciate retaining or posterior stabilised design. Computer-aided design (CAD) implant models were either provided by the manufacturer or obtained from 3D scanned retrieved implants. Tibial and femoral CAD models were used in mbRSA to capture pose data in the form of Cartesian coordinates at all follow-ups for each patient. Coordinate data was manually entered into a 3D modeling software (Geomagic Studio) to position the implant components in virtual space as presented in the RSA examinations. PE wear was measured over successive follow-ups as the linear change in joint space, defined as the shortest distance between the tibial baseplate and femoral component, independently for medial and lateral sides. A linear best-fit was applied to each patient9s wear data; the slope of this line determined the annual wear rate per individual patient. Wear rates were averaged to provide a mean rate of in vivo wear for the Triathlon PE bearing. Mean linear wear per annum across all 72 patients was 0.088mm/yr (SD: 0.271 mm/yr) for the medial condyle and 0.032 mm/yr (SD: 0.230 mm/yr) for the lateral condyle. Cumulative linear wear at the 2-year follow-up interval was 0.207mm (SD: 0.565mm) and 0.068mm (SD: 0.484mm) for the medial and lateral condyles, respectively. Linear PE wear measurements using mbRSA and Geomagic Studio resulted in 0.056mm/yr additional wear on the medial condyle than the lateral condyle. Large standard deviations for yearly wear rates and cumulative measurements demonstrate this method does not yet exhibit the accuracy needed to provide short-term in vivo wear measurement. Inter-patient variability from RSA examinations is likely a source of error when dealing with such small units of measure. Further analysis on patient age and body mass index may eliminate some variability in the data to improve accuracy. Despite high standard deviations, the results from this research are in proximity to previously reported linear wear measurements 0.052mm/yr and 0.054mm/yr. Linear wear analysis will continue upon completion of >100 patients, in addition to volumetric PE wear over the entire articulating surface.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.098
GPT teacher head0.262
Teacher spread0.164 · 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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