Evidence map›Paper›PMID 40162048›Full record

ArticleJournal of orthopaedics2025

Appropriately planned resection depth can impact outcomes after robotic total knee arthroplasty.

Jayson Zadzilka, Bernard Stulberg, Brian Davis

Abstract read
In one paragraph

Article in Journal of orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Jayson ZadzilkaDepartment of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, USA.
Bernard StulbergDepartment of Orthopedic Surgery, Crystal Clinic Orthopaedic Center, Fairlawn, Ohio, USA.
Brian DavisDepartment of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: There are many factors to consider while planning and executing a successful total knee arthroplasty. One of these, the amount of bony resection, is determined in part based on the patients' anatomy and preoperative deformity. Utilizing intraoperative technology such as robotics allows for resection to be done accurately. Therefore, the goal of this study was to investigate the relationship between tibial & femoral resection depths and postoperative outcomes. Additionally, a quantitative method for preoperatively determining the level of resection depth needed was developed. Methods: A de-identified dataset containing 107 robotic total knee arthroplasty cases was reviewed. Preoperative demographics, preoperative planning details, and sub-scale scores from the Knee Society Scoring System were reviewed. Analysis was performed to find significant associations with the sub-scale scores. Additionally, multiple regression models were developed to predict resection depth values. Results: Associations were found between femoral resection depth and Satisfaction & Function scores three months postoperatively. Additionally, Satisfaction and Function were 6 % and 16 % higher respectively when the native alignment strategy was used rather than mechanical alignment of the lower limb. Three-month Function scores were also 6 % higher for males than females. The models to predict resection depth included alignment strategy, preoperative knee deformity, and gender as the significant contributors. Conclusion: Tibial and femoral resection depth can influence postoperative outcomes. Therefore, it is important to understand what factors contribute to the determination of how much bone should be resected. With that information, patient-specific preoperative plans can be developed with the intent of optimizing postoperative outcomes.

Indexed as

FunctionPreoperative planningResection depthRobotic total knee arthroplastySatisfaction

Identifiers

PMID40162048
PMCPMC11952848

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Registered trials

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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.