Evidence map›Paper›PMID 32309759›Full record

ArticleJB & JS open access

Comparing the Use of Axillary Radiographs and Axial Computed Tomography Scans to Predict Concentric Glenoid Wear.

Kofi D Agyeman, Paul DeVito, Emmanuel McNeely, Andy Malarkey, Michael J Bercik, Jonathan C Levy

Abstract read
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Article in JB & JS open access. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Kofi D AgyemanMiller School of Medicine, University of Miami, Miami, Florida.
Paul DeVitoHoly Cross Orthopedic Institute, Fort Lauderdale, Florida.
Emmanuel McNeelyCharles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida.
Andy MalarkeyHoly Cross Orthopedic Institute, Fort Lauderdale, Florida.
Michael J BercikLancaster Orthopedic Group, Lancaster, Pennsylvania.
Jonathan C LevyHoly Cross Orthopedic Institute, Fort Lauderdale, Florida.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAxillary radiographs traditionally have been considered sufficient to identify concentric glenoid wear in osteoarthritic shoulders; however, with variable glenoid wear patterns, assessment with use of computed tomography (CT) has been recommended. The purpose of the present study was to compare the use of axillary radiographs and mid-glenoid axial CT scans to identify glenoid wear.

methodsPreoperative axillary radiographs and mid-glenoid axial CT scans for 330 patients who underwent anatomic total shoulder arthroplasty were reviewed. Five independent examiners with differing levels of experience characterized the glenoid morphology as either concentric or eccentric. The morphologies determined with use of axillary radiographs and CT scans were assessed for correlation, and both intraobserver and interobserver consistency were calculated.

resultsConcentric wear identified with use of radiographs was confirmed with use of CT scans in an average of 61% of cases (range, 53% to 76%). Intraobserver consistency averaged 75% for radiographs and 73% for CT scans. There was significant interobserver consistency, as higher levels of training corresponded with greater consistency between imaging analyses (p < 0.001). The most senior observer identified the highest proportion of concentric wear on radiographs (p < 0.001), showed the greatest consistency between attempts when using CT (p < 0.001), and had the greatest agreement of radiographs and CT evaluating glenoid morphology (p < 0.001).

conclusionsFor the experienced shoulder surgeon, concentric glenoid wear identified on axillary radiographs will appear concentric on 2-dimensional CT in approximately 75% of cases. Obtaining a CT scan to confirm glenoid wear patterns most greatly benefits less-experienced surgeons. Across all levels of experience, axillary radiographs and single-slice, mid-glenoid CT scans appear insufficient for consistently predicting wear patterns. LEVEL OF EVIDENCE: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Identifiers

PMID32309759
PMCPMC7147633

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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.