Evidence map›Paper›PMID 37064848›Full record

ReviewSisli Etfal Hastanesi tip bulteni2023

Split Bolus Method in Computerized Tomography.

Huseyin Ozkurt, Sidal Ozdogan, Eyup Camurcuoglu

Open access · hybridAbstract readReview
In one paragraph

Review in Sisli Etfal Hastanesi tip bulteni, 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
0.3field-weighted citation impact, top 42% 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

3 authors at 1 institution in 2 countries.

Huseyin OzkurtDepartment of Radiology, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye.
Sidal OzdoganDepartment of Radiology, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye.
Eyup CamurcuogluDepartment of Radiology, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye.
University of Health Sciences Antigua · AG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The split-bolus method in computed tomography (CT) is the method used in the evaluation of renal dynamic enhancement stages and in the detection of pathologies. When designing the CT urography technique, there are several important options such as single-bolus and split bolus techniques. The single-bolus method consists of three separate post-contrast phases: arterial, nephrographic, and excretory (pyelogram), as a result raising the total radiation dose imparted to patients. On the other hand, in the split-bolus technique, the contrast dose is divided into several separate administrations to obtain the nephrographic and excretory phases simultaneously. With the split-bolus technique, by reducing the radiation dose and the number of phases that the patient will be exposed to, urinary system evaluation and the whole abdomen pathological evaluations can be performed. The device to be used in imaging must be a tomography device with at least 16 Multidetector CT sections. The bolus tracking method is one of the most accurate contrast delivery methods for renal dynamics and the split-bolus technique. Automatic dose calibration is used.

Indexed as

Bolus trackingcomputed tomographycomputed tomography urographycontrast injectionsdoseexcretory phase (urographic phase)kidneynephrogenic phase (parenchimal)renal arterial phaserenal dynamicssplit-bolus methodurinary systemvenous phase

Identifiers

PMID37064848
PMCPMC10098405
OpenAlexW4312750148

What OpenQuestion holds

Textmetadata
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.