Evidence map›Paper›PMID 37359179›Full record

ArticleEuropean journal of radiology open2023

Reducing ' probably benign ' assessments in normal mammograms: The role of radiologist experience.

Mohammad A Rawashdeh, Patrick C Brennan

Open access · goldAbstract read
In one paragraph

Article in European journal of radiology open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  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

2 authors at 2 institutions in 3 countries.

Mohammad A RawashdehFaculty of Health Sciences, Gulf Medical University, Ajman, United Arab Emirates.
Patrick C BrennanMedical Image Optimisation and Perception Group (MIOPeG), Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Gulf Medical University · AEUniversity of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale and objectives: to investigate the relationship between radiologists' experience in reporting mammograms, their caseloads, and the classification of category '3' or 'Probably Benign' on normal mammograms. Materials and Methods: A total of 92 board-certified radiologists participated. Self-reported parameters related to experience, including age, years since qualifying as a radiologist, years of experience reading mammograms, number of mammograms read per year, and hours spent reading mammograms per week, were documented. To assess the radiologists' accuracy, "Probably Benign fractions" was calculated by dividing the number of "Probably Benign findings" given by each radiologist in the normal cases by the total number of normal cases Probably Benign fractions were correlated with various factors, such as the radiologists' experience. Results: The results of the statistical analysis revealed a significant negative correlation between radiologist experience and 'Probably Benign' fractions for normal images. Specifically, for normal cases, the number of mammograms read per year (r = -0.29, P = 0.006) and the number of mammograms read over the radiologist's lifetime (r = -0.21, P = 0.049) were both negatively correlated with 'Probably Benign' fractions. Conclusion: The results indicate that a relationship exists between increased reading volumes and reduced assessments of 'Probably Benign' in normal mammograms. The implications of these findings extend to the effectiveness of screening programs and the recall rates.

Indexed as

MammographyProbably benignRadiologist performance

Identifiers

PMID37359179
PMCPMC10285087
OpenAlexW4380853711

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.