Evidence map›Paper›PMID 35893286›Full record

ArticleJournal of personalized medicine2022

Surgeon Dominated Design Can Improve the Accuracy of Patient-Specific Instruments in Kinematically Aligned TKA.

Liang Wen, Zhiwei Wang, Desi Ma, Tiebing Qu

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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.

Liang WenDepartment of Orthopeadics, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.ORCID 0000-0003-4006-0107
Zhiwei WangDepartment of Orthopeadics, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.ORCID 0000-0002-5668-075X
Desi MaDepartment of Orthopeadics, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.
Tiebing QuThe Center of Diagnosis and Treatment for Joint Disease, China Rehabilitation Research Center, Beijing 100068, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise bone resection is mandatory for kinematically aligned total knee arthroplasty (KA-TKA). This study is to investigate whether surgeon-dominated design can alter the accuracy of patient-specific instrumentation (PSI) in KA-TKA compared with the engineer design. A total of 24 patients (24 knees) who underwent KA-TKA in our institution were assigned to an engineer design group (10 knees) and surgeon design group (14 knees) chronologically. A novel portable medical-engineer interactive application can greatly enhance the surgeon's participation in PSI design. The bone resection discrepancies were used to evaluate the accuracy of PSI in bone resection. The overall discrepancy of bone resection was reduced by surgeon-designed PSI compared to engineer-designed PSI by 0.33 mm. Surgeon-designed PSI seems to reduce the outliers in terms of relative discrepancies in bone resection as well, but it does not reach statistical significance. Moreover, surgeon-designed PSI could significantly improve the accuracy of PSI in the restoration of the joint line in terms of medial proximal tibial angle and mechanical lateral distal femoral angle. This study indicates that the dominance of surgeons in both PSI design and subsequent surgical operation should be emphasized in efforts to improve the accuracy of PSI.

Indexed as

accuracybone resectiondesignkinematic alignmentpatient specific instrumentationtotal knee arthroplasty

Identifiers

PMID35893286
PMCPMC9332028

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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.