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Record W4415570975 · doi:10.1302/1358-992x.2025.11.013

KNEE KINEMATICS DURING LAXITY TESTING FOLLOWING ROBOTIC-ASSISTED AND CONVENTIONAL MECHANICAL TOTAL JOINT REPLACEMENT

2025· article· en· W4415570975 on OpenAlexaff
Eve Donnelly, Mark Richards, Samira Vakili, Steven J. MacDonald, Ryan Willing, Brent A. Lanting

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWestern University
Fundersnot available
KeywordsKinematicsTotal knee replacementRange of motionValgusArthroplastyKnee JointJoint replacementImplant

Abstract

fetched live from OpenAlex

Robotic-assisted surgery has become an increasingly attractive option for orthopaedic surgeries, specifically in its use for total knee replacement (TKR). Despite the allure, characterization of the outcomes of robotic-assisted surgery against conventional manual methods remains to be investigated. Therefore, this study aims to characterize kinematics of TKR knees following robotic-assisted and conventional arthroplasty through loaded laxity testing. We hypothesize that robotic-assisted TKR will result in less variable kinematic behavior at end range of motion than its conventional counterparts. Seven pairs of TKR knees were used in this study. For each pair, one surgery was performed using conventional manual methods, while the contralateral side utilized a VELYS robotic-assisted solution (DePuy Synthes). All TKRs were performed by the same surgeon and used a cruciate retaining rotating platform (CR RP) implant system. The knees were mounted onto a VIVO joint motion simulator (Advanced Mechanical Technologies Inc.). Once installed, specimens were pre-conditioned via a baseline loading protocol that involved cycling though the flexion arc from 15 to 90 degrees, while reducing the joint with 30 N of compression. Next, laxity was assessed via applied isolated (1) 4 Nm internal/ external (IE) rotation torques, (2) 8 Nm varus/ valgus (VV) rotation torques, and (3) 40 N anterior, and 80 N posterior forces. Each was performed at discrete positions of 15, 30, 60 and 90 degrees of knee flexion. Laxity outputs were adjusted for joint behavior during baseline to mitigate and mounting biases and account for specimen-specific joint behavior, and conventional TKR and robotic-assisted TKR knees were compared via paired two-tailed t-tests for each combination of flexion angle and tested degree of freedom. At 90° flexion, external rotation laxity was significantly different between the groups (P=0.04), with conventional TKR knees exhibiting greater rotation (26.5° ± 9.1°) than robotic- assisted TKR knees (18.3° ± 3.5°). This is complemented by the observed trend of the difference between conventional and robotic TKR knee external rotation laxity limits increasing with each flexion angle”: 15.8° ± 8.1° [conventional] and 14.1° ± 4.1° [robotic-assisted] at 15 degrees, 20.2° ± 7.2° [conventional] and 18.5° ± 5.6° [robotic-assisted] at 30 degrees, and 21.9° ± 7.2° [conventional] and 19.0° ± 5.2° [robotic-assisted] at 60 degrees. No other statistically significant differences were identified. The results of the current study suggest that while conventionally done TKR may result in comparable joint laxity than those done using conventionally methods, through most of the flexion arc, the latter provides greater stability in external rotation in high flexion (namely 90°). However, observed laxity differences may be compensated for in the opposite direction, resulting in a balanced total laxity envelope. These findings could provide a greater understanding of patient outcomes and inform surgical decision making to cater to a patient's desired functional outcome.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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.

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
Published2025
Admission routes1
Has abstractyes

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