Evidence map›Paper›PMID 27995287›Full record

ReviewDer Radiologe2017

[Noninfectious differential diagnoses of pneumonia].

A Wielandner, P Agarwal, A Toelly, C Bardach

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Der Radiologe, 2017. 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. [Pulmonary infiltrates following accidental immobilzation].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024
    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

4 authors.

A WielandnerUniversitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währingergürtel 18-20, 1090, Wien, Österreich. alice.wielandner@meduniwien.ac.at.
P AgarwalUniversitätsklinik für Radiologie, Medizinische Universität Freiburg, Hugstetterstraße 55, 79106, Freiburg, Deutschland.
A ToellyUniversitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währingergürtel 18-20, 1090, Wien, Österreich.
C BardachAbteilung für Radiologie, Krankenhaus Hietzing, Wolkersbergenstraße 1, 1130, Wien, Österreich.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn patients with a clinical suspicion of pneumonia, typical clinical and laboratory features along with the detection of infiltrates on chest X‑ray are as a rule considered diagnostic and therapy is immediately initiated; however, studies have shown that in up to 5% of patients with an initial suspicion of pneumonia, another noninfectious pulmonary disease was the underlying cause. Early recognition and differentiation of diseases mimicking pneumonia are prerequisites for an adequate therapy.

objectiveThe aim of this review is to present the important noninfectious differential diagnoses of pneumonia and to provide the reader with tools for a systematic diagnostic approach. MATERIAL AND

methodA literature search was carried out.

resultsAs alterations in the lungs often result in similar imaging appearances and a differentiation between transudates, exsudates, blood and cells is not feasible by chest X‑ray or CT, a systematic approach is essential to make an appropriate diagnosis. Hence, consideration of the temporal course, predominant pattern, distribution of findings, additional findings and clinical presentation are indispensable.

Indexed as

Diagnosis, DifferentialDiagnostic ErrorsHumansLungPneumoniaRadiographic Image EnhancementRadiography, ThoracicTomography, X-Ray ComputedChest X rayComputed tomographyPredominant patternTemporal courseTreatment resistant

Identifiers

PMID27995287
PMCPMC7095929

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.