Evidence map›Paper›PMID 30200068›Full record

Trial reportMedicine2018

Craniocervical computed tomography angiography with adaptive iterative dose reduction 3D algorithm and automatic tube current modulation in patients with different body mass indexes.

Shujing Yu, Jing Zheng, Li Zhang

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 43% 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, 3 citations in OpenAlex.

  1. Article
  2. 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

3 authors at 1 institution in 1 country.

Shujing YuDepartment of Diagnostic CT, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Jing Zheng
Li Zhang
Cangzhou Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to investigate the feasibility of head and neck computed tomography angiography (CTA) using the 80-kV tube voltage and the adaptive iterative dose reduction (AIDR) 3D algorithm in patients with different body mass indexes (BMIs).From November 2016 to January 2017, 128 consecutive patients scheduled for head and neck CTA examinations were randomized into the 100-kV group (n = 60) and the 80-kV group (n = 68). Both groups used the automatic tube current modulation technique and the AIDR 3D algorithm. The patients were further grouped as slender (BMI < 22 kg/m), normal weight (22 kg/m≤BMI < 25 kg/m), and overweight (BMI ≥25 kg/m). The image quality and the radiation dose of each subgroup were analyzed.The images of the head and neck vessels and the brain tissue obtained with 100 kV were all of diagnostic quality. Slender and normal weight patients imaged with 80 kV also produced images of diagnostic quality. However, 80 kV in the overweight patients failed to produce images of diagnostic quality. The radiation dose in the patients imaged with 80 kV was significantly decreased in comparison with those imaged with 100 kV. The effective dose was 0.36 ± 0.06 and 0.41 ± 0.05 mSv in the slender and normal weight patients imaged with 80 kV.Head and neck CTA scanning with 80 kV, automatic tube current modulation, and AIDR 3D algorithm can produce high quality images with reduced radiation dose in slender or normal weight patients.

Indexed as

AlgorithmsBody Mass IndexRadiation DosageAutomationCerebral AngiographyComputed Tomography AngiographyFeasibility StudiesFemaleHeadHumansMaleMiddle AgedNeckOverweight

Identifiers

PMID30200068
PMCPMC6133620
OpenAlexW2891691550

What OpenQuestion holds

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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.