Evidence map›Paper›PMID 31836955›Full record

ReviewEmergency radiology2020

Duodenal emergencies: utility of multidetector CT with 2D multiplanar reconstructions for challenging but critical diagnoses.

Mikhael Polotsky, Harshna V Vadvala, Elliot K Fishman, Pamela T Johnson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Emergency radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 38% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Mikhael PolotskyDepartment of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University, 601 North Caroline Street, Baltimore, MD, 21287, USA.
Harshna V VadvalaDepartment of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University, 601 North Caroline Street, Baltimore, MD, 21287, USA. hvadval1@jhmi.edu.ORCID http://orcid.org/0000-0002-2067-8201
Elliot K FishmanDepartment of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University, 601 North Caroline Street, Baltimore, MD, 21287, USA.
Pamela T JohnsonDepartment of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University, 601 North Caroline Street, Baltimore, MD, 21287, USA.
Johns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Duodenal pathology is an infrequent cause of acute abdominal pain for which patients present to the emergency department. Critical pathology on multidetector CT (MDCT) may be overlooked if the radiologist does not carefully evaluate the duodenum as part of the search pattern. Optimal MDCT protocols include intravenous contrast with multiplanar reconstructions (MPRs). A variety of etiologies ranging from infection to malignancy can involve the duodenum, for which interrogation with MPRs is most helpful given the anatomy and complex relationship with surrounding structures. The purpose of this review article is to highlight the importance of CT acquisition with multiplanar reconstructions and review the spectrum of emergent duodenal pathology, with the goal of ensuring accurate and timely diagnosis to best guide patient management.

Indexed as

Abdominal PainContrast MediaDuodenal DiseasesEmergency Service, HospitalHumansMultidetector Computed TomographyContrast MediaAbdominal painAcute abdomenDuodenumMultidetector CTMultiplanar reconstruction

Identifiers

PMID31836955
OpenAlexW2996706914

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.