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Record W2883051581 · doi:10.1097/corr.0000000000000399

Radiostereometric Analysis Permits In Vivo Measurement of Very Small Levels of Wear in TKA

2018· article· en· W2883051581 on OpenAlexafffund
Matthew G. Teeter, Jacob Wihlidal, Richard W. McCalden, Xunhua Yuan, Steven J. MacDonald, Brent A. Lanting, Douglas D.R. Naudie

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchStryker
KeywordsMedicineImplantSurgeryRadiographyOrthodonticsOrthopedic surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Revision of TKA as a result of polyethylene wear is decreasing, but long-term wear performance of polyethylene is still a topic of interest to surgeons and device manufacturers seeking to improve longevity. Measuring wear of modern, wear-resistant implants has been described using radiostereometric analysis (RSA). Performing in vivo measurements would establish whether implant retrieval studies are representative of wear in well-performing knees. QUESTIONS/PURPOSES: For a single knee implant system, we sought to determine (1) the linear wear rate using RSA; (2) the association between demographic factors and wear rate; and (3) the association between limb alignment and wear rate. METHODS: A total of 49 patients with a minimum followup of 10 years (median, 12 years; range, 10-20 years) were retrospectively selected. During the examined period, 4082 TKAs were performed of which 2085 were the implant examined in this study. There were 71 of these patients who met the criteria including an available full-leg radiograph postoperatively, and 34 of these patients returned for examination along with 15 additional from a separate RSA study that also met the criteria. All patients received a posterior-stabilized, cobalt-chromium-on-conventional polyethylene total knee implant from a single implant system, which was the most commonly used at our institution at the time. Patients underwent standing RSA examinations from 0° to 120° of flexion at a single time point without the use of marker beads. Linear wear rates (including creep) were measured based on intersections between the femoral component and tibial insert models. Associations between wear and patient age at surgery, sex, height, weight, body mass index, tibial insert size, and limb alignment were examined. RESULTS: Using the maximum linear wear rate from any flexion angle, the lateral rate was 0.047 mm/year (interquartile range [IQR], 0.034-0.066 mm/year) and the medial rate was 0.052 mm/year (IQR, 0.040-0.069 mm/year). Using the median of the linear wear rates across all flexion angles, the lateral rate was 0.027 mm/year (IQR, 0.017-0.046 mm/year) and the medial rate was 0.038 mm/year (IQR, 0.022-0.054 mm/year). This rate for males was 0.049 mm/year medially (IQR, 0.042-0.077 mm/year) and 0.032 mm/year laterally (IQR, 0.026-0.059 mm/year), and for females was 0.027 mm/year medially (0.016-0.039 mm/year) and 0.020 mm/year laterally (IQR, 0.013-0.032 mm/year). The wear rate for males was greater medially (difference = 0.022 mm/year, p < 0.001) and laterally (difference = 0.012 mm/year, p = 0.008). There were associations between greater wear and increasing height (ρ = 0.48, p < 0.001 medially and ρ = 0.30, p = 0.04 laterally), decreasing body mass index (ρ = -0.31, p = 0.03 medially), and greater implant size (ρ = 0.34, p = 0.02 medially). Increasingly varus leg alignment was associated with greater medial wear (ρ = 0.33, p = 0.02). CONCLUSIONS: Greater wear rates were associated with demographic factors and leg alignment. Further RSA wear studies of other modern implant systems would provide complementary information to retrieval studies and valuable data on wear resistance. CLINICAL RELEVANCE: Good wear resistance was demonstrated by well-performing implants in patients at long-term followup with wear magnitudes in agreement with reported values from retrieval studies.

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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)0.001
metaresearch head score (Gemma)0.003
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.190
GPT teacher head0.427
Teacher spread0.237 · 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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Citations11
Published2018
Admission routes2
Has abstractyes

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