Evidence map›Paper›PMID 39318404›Full record

ReviewShoulder & elbow2024

Advanced technology in shoulder arthroplasty.

Jack Zhong, Michael Boin, Joseph D Zuckerman

Abstract readReview
In one paragraph

Review in Shoulder & elbow, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Jack ZhongDepartment of Orthopaedic Surgery, Division of Shoulder & Elbow Surgery, NYU Langone Health, New York, NY, USA.ORCID https://orcid.org/0000-0001-5857-6094
Michael BoinDepartment of Orthopaedic Surgery, Division of Shoulder & Elbow Surgery, NYU Langone Health, New York, NY, USA.
Joseph D ZuckermanDepartment of Orthopaedic Surgery, Division of Shoulder & Elbow Surgery, NYU Langone Health, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Glenoid component positioning is an important and challenging aspect of total shoulder arthroplasty. The use of freehand technique with standard instrumentation or preoperative planning based on 2-dimensional computed tomography (CT) scans provides an opportunity for improvement in terms of component accuracy, precision, and deformity correction. These techniques have produced varying outcomes. Methods: Preoperative planning software (PPS), patient specific instrumentation (PSI), and intraoperative navigation (NAV) have been developed to improve the accuracy of implant placement and deformity correction with the ultimate goals of improved patient outcomes and implant longevity. Literature search was conducted on published and available studies comparing the accuracy of glenoid component placement and improvements in surgical and patient outcomes amongst the aforementioned techniques. Results: PPS, PSI, and NAV have demonstrated improved accuracy over freehand techniques with standard instrumentation. However, data demonstrating the clinical benefit and cost effectiveness of these new technologies are lacking. Discussion: In this paper, we reviewed the evidence available to answer the question of whether or not advanced shoulder arthroplasty technologies have been beneficial and reviewed future technologies in development such as virtual/mixed-reality and robotic assisted shoulder surgery. Level of Evidence: 4.

Indexed as

anatomicarthroplastyaugmented realitycomputer assisted navigationglenoid componentguide pinpatient specific instrumentationpreoperative planning softwarereverseroboticshouldervirtual reality

Identifiers

PMID39318404
PMCPMC11418667

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.