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Record W4416441655 · doi:10.4055/cios25143

Cementless Versus Cemented Fixation in Total Knee Arthroplasty: Analysis of Regional Tibial Bone Density and Clinical Outcome

2025· article· en· W4416441655 on OpenAlexaboutno aff
Daniel W. Wong, Qunn Jid Lee, Chi-kin Lo, Kenneth Wing-Kin Law, Esther Chang, Yiu-Chung Wong

Bibliographic record

VenueClinics in Orthopedic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCondyleFixation (population genetics)Bone resorptionTotal knee arthroplastyTibiaResorption

Abstract

fetched live from OpenAlex

Background: Cementless fixation in total knee arthroplasty (TKA) has theoretical advantages of being biological and bone preserving, but some surgeons are less confident in using it given previous reports of high failure rates in some implant designs. This study aimed to investigate the effect of fixation method on tibial bone density, clinical outcome, and survivorship. The main research question was whether fixation method would affect the postoperative change in tibial bone density in TKA. Methods: This study analyzed 53 cementless TKAs and 53 cemented TKAs of the same brand (Triathlon, Stryker). Digital radiological densitometry (DRD) was used to quantify the changes in regional tibial bone density (RTBD) within the first 2 years. Clinical outcome scores and survivorship were recorded. Results: = 0.029). Clinical outcome scores (Knee Society Score, Western Ontario and McMaster Universities Osteoarthritis Index, and Forgotten Joint Score) were similar. No case of aseptic loosening was reported. Conclusions: Proximal tibial bone resorption was common in both cementless and cemented TKAs. Cementless fixation preserved more tibial metaphyseal bone globally at 6 months and at the lateral tibial condyle at 24 months. Its early clinical outcomes and survivorship were comparable to those of cemented fixation.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.022
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.068
GPT teacher head0.375
Teacher spread0.307 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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