Evidence map›Paper›PMID 36759382›Full record

ArticleJournal of digital imaging2023

The FIND Program: Improving Follow-up of Incidental Imaging Findings.

Kaitlin M Zaki-Metias, Jeffrey J MacLean, Alexander M Satei, Serguei Medvedev, Huijuan Wang, Christopher C Zarour, Paul J Arpasi

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
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, 11 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Kaitlin M Zaki-MetiasDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA. kaitlinmzaki1@gmail.com.ORCID 0000-0003-0098-2422
Jeffrey J MacLeanDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.ORCID 0000-0002-6868-7959
Alexander M SateiDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.ORCID 0000-0001-9124-7890
Serguei MedvedevDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.ORCID 0000-0001-6093-3206
Huijuan WangDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.ORCID 0000-0002-2813-3847
Christopher C ZarourDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.ORCID 0000-0001-6192-2920
Paul J ArpasiDepartment of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA.
Trinity Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Incidental findings are findings identified on imaging which are unrelated to the original reason for examination and require follow-up. The Radiology Finding Incidental Disease (FIND) Program was designed to track and improve follow-up of incidental imaging findings. The purpose of this study was to determine the frequency of incidental findings on cross-sectional imaging and the adherence to suggested follow-up of incidental findings prior to and after implementation of a structured reporting and tracking system. A retrospective analysis of 2000 patients with computed tomographic cross-sectional imaging was performed: 1000 patients prior to implementation of the FIND Program and 1000 patients 1 year after establishment of the program. Data collected included the frequency of incidental findings, inclusion of follow-up recommendations in the radiology report, and adherence to suggested follow-up. There was a higher rate of completion of recommended follow-up imaging in the post-implementation group (34/67, 50.7%) compared to the pre-implementation (16/52, 30.8%) (p = 0.03). Implementation of an incidental findings tracking program resulted in improved follow-up of incidental imaging findings. This has the potential to reduce the burden of clinically significant incidental findings possibly resulting in later presentation of advanced disease.

Indexed as

RadiologyTomography, X-Ray ComputedFollow-Up StudiesHumansRadiographyRetrospective StudiesArtificial intelligenceComputed tomographyIncidental findingsIncidental imaging findingsNatural language processingNatural language understanding

Identifiers

PMID36759382
PMCPMC10287591
OpenAlexW4319827352

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.