Evidence map›Paper›PMID 37745959›Full record

ArticleArthroplasty today2023

Laxity, Balance, and Alignment of a Simulated Kinematic Alignment Total Knee Arthroplasty.

Adam I Edelstein, Edgar A Wakelin, Christopher Plaskos, Linda I Suleiman

Abstract read
In one paragraph

Article in Arthroplasty today, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Adam I EdelsteinDepartment of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Edgar A WakelinCorin Ltd., Raynham, MA, USA.
Christopher PlaskosCorin Ltd., Raynham, MA, USA.
Linda I SuleimanDepartment of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Kinematic alignment (KA) and related personalized alignment strategies in total knee arthroplasty (TKA) target restoration of native joint line obliquity and alignment. In practice, deviations from exact restoration of the prearthritic joint surface are tolerated for either the femur or tibia to achieve ligamentous balance. It remains unknown what laxity, balance, and alignment would result if a pure resurfacing of both femur and tibia were performed in a KA TKA technique. Methods: We used data from 382 robot-assisted TKA performed with a digital joint tensioner to simulate TKA with a pure resurfacing KA technique for both femur and tibia. All knees had the posterior cruciate ligament retained. Knees were subdivided into 4 groups based on preoperative coronal alignment: valgus, neutral, varus, and high varus. Medial and lateral laxity in extension and flexion, balance in extension and flexion, and coronal plane alignment were compared between groups using analysis of variance testing. Results: In simulated pure resurfacing KA TKA across a range of preoperative coronal plane deformities, only 11%-31% of knees would have mediolateral extension ligament balance within ±1 mm, and 20%-41% would have a medial flexion gap that is looser than the lateral flexion gap. Over 45% of knees would have coronal hip-knee-ankle angle >3 degrees from mechanical neutral. Conclusions: In simulations of pure resurfacing KA TKA, there was wide variability in the resulting laxity and alignment outcomes. Most knees had alignment and balance outcomes outside of normally accepted ranges. Techniques that deviate from pure resurfacing in order to achieve balance appear favorable.

Indexed as

AlignmentBalanceKinematic alignmentSimulationTotal knee arthroplasty

Identifiers

PMID37745959
PMCPMC10514418

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.