Evidence map›Paper›PMID 39300891›Full record

ReviewZhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery2024

[Research progress in Oxford unicompartmental knee arthroplasty for partial thickness cartilage loss in medial compartment of knee joint].

Wenbin Huang, Dianqi Liu, Weijie Lu

Abstract readReview
In one paragraph

Review in Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wenbin HuangDepartment of Joint Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Guangdong, 510120, P. R. China.
Dianqi LiuDepartment of Joint Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Guangdong, 510120, P. R. China.
Weijie LuDepartment of Joint Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Guangdong, 510120, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To summarize the research progress of Oxford unicompartmental knee arthroplasty (UKA) in treating partial thickness cartilage loss (PTCL) in the medial compartment of the knee joint, aiming to further clarify the indications and optimize the effectiveness of Oxford UKA. Methods: A comprehensive review of recent domestic and international literature on Oxford UKA for PTCL in the medial compartment of the knee joint was conducted to summarize its application and research advancements. Results: Based on current researches, the main indication for Oxford UKA is full thinckness cartilage loss in the medial compartment of the knee joint. Although it has shown certain effectiveness in treating PTCL in the medial compartment of the knee joint, there are also reports of opposite conclusions. Therefore, there is still controversy over whether Oxford UKA can be chosen for PTCL, and the large-sample and multi-center studies are needed to further clarify the controversy. Studies indicate that accurate preoperative assessment of cartilage damage severity is crucial for selecting appropriate candidates for Oxford UKA to optimize postoperative effectiveness. Conclusion: Oxford UKA may represent an effective treatment for patients with PTCL in the medial compartment of the knee joint. However, strict patient selection and precise preoperative evaluation are essential to ensure surgical success and long-term effectiveness.

Indexed as

Arthroplasty, Replacement, KneeCartilage, ArticularKnee JointKnee ProsthesisOsteoarthritis, KneeHumansTreatment OutcomeKnee jointosteoarthritisOxford unicompartmental knee arthroplastypartial thickness cartilage loss

Identifiers

PMID39300891
PMCPMC11440162

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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.