Evidence map›Paper›PMID 28681117›Full record

ReviewZeitschrift fur Rheumatologie2017

[Dual-energy computed tomography diagnostics for gout].

H J Rech, A Cavallaro

Abstract readReview
PubMed Publisher
In one paragraph

Review in Zeitschrift fur Rheumatologie, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. 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 1 institution in 1 country.

H J RechStudien- und Infusionsambulanz, Medizinische Klinik 3, Rheumatologie & Immunologie, Friedrich-Alexander Universität Erlangen-Nürnberg, Ulmenweg 18, 91054, Erlangen, Deutschland. juergen.rech@uk-erlangen.de.
A CavallaroRadiologisches Institut, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Deutschland.
Friedrich-Alexander-Universität Erlangen-Nürnberg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNon-invasive imaging modalities are used for the morphological and functional evaluation and diagnosis of gout; however, none of these methods are sufficiently sensitive or specific for the diagnosis of gout. By the use of dual-energy computed tomography (DECT) the direct visualization of sodium urate deposition is now possible. Moreover, it is possible to show the different pathological situations, disease activity and monitoring of tophaceous gout. The positive DECT scan became one of the 2015 ACR/EULAR classification criteria for gout 2 years ago. MATERIAL AND

methodsThe literature search from 2009-2017 in PubMed, with the search criteria "Dual Energy CT and Gout" revealed 77 publications on the DECT method.

resultsThe DECT technique has a high sensitivity (90-100%) and a high specificity (83-89%) for diagnosing gout. The development of a semiquantitative scoring method also enables measurement of the severity of urate deposits at specific sites. CONCUSION: The gold standard for the diagnosis of gout is still the detection of monosodium urate monohydrate crystals by polarizing microscopy after a joint punction; however with the DECT it is possible to non-invasively visualize, measure and score urate deposition and is therefore suitable for estimating the severity and prognosis. Although not clinical validated, DECT may sometimes help to detect gout. More studies should be conducted.

Indexed as

CrystallizationFollow-Up StudiesGoutHumansJointsPrognosisTomography, X-Ray ComputedUric AcidUric AcidClassification criteriaCourse assessmentNon-invasive methodSodium urate depositionTherapy monitoring

Identifiers

PMID28681117
OpenAlexW2732464681

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.