Evidence map›Paper›PMID 38904916›Full record

ArticleRadiological physics and technology2024

Data analysis of average glandular dose in mammography toward revision of the diagnostic reference level of Japan.

Toru Negishi, Yusuke Koba, Kiyomitsu Shinsho, Daisuke Fujise, Masahiro Sai, Hiroko Nishide

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Article in Radiological physics and technology, 2024. 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
–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

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.

Toru NegishiDepartment of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.ORCID http://orcid.org/0000-0003-2134-2547
Yusuke KobaInstitute of Radiological Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan. koba.yusuke@qst.go.jp.ORCID http://orcid.org/0000-0001-8575-0465
Kiyomitsu ShinshoDepartment of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.ORCID http://orcid.org/0000-0001-6439-991X
Daisuke FujiseDepartment of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.ORCID http://orcid.org/0009-0006-6956-0032
Masahiro SaiJapan Central Organization On Quality Assurance of Breast Cancer Screening, Nagoya, Japan.
Hiroko NishideJapan Central Organization On Quality Assurance of Breast Cancer Screening, Nagoya, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnostic reference level (DRL) for mammography for 2015 and 2020 has been published by J-RIME. More new dose studies are needed to revise the next DRL. In preparation for the next revision of the DRL for mammography, this study investigated data from the Japan Central Organization on Quality Assurance of Breast Cancer Screening on the mean average glandular dose (AGD) for institutional image accreditation in 2019-2023 and the relationship between the average at eligible institutions to date and the type of breast X-ray system. The 95th percentile values of the AGD distributions for the Computed Radiography (CR) and Flat Panel Detector (FPD) systems were 2.5 mGy and 2.0 mGy, respectively. Moreover, it is assumed that AGD is decreasing due to the spread of FPD systems, and it is expected that the further spread of FPD systems and systems with W/Rh target/filter will reduce AGD in future.

Indexed as

Diagnostic Reference LevelsMammographyRadiation DosageBreastBreast NeoplasmsData AnalysisFemaleHumansJapanAverage glandular doseDiagnostic reference level (DRL)Mammography

Identifiers

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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.