Evidence map›Paper›PMID 26610683›Full record

ArticleDer Radiologe2015

[Gynecological tumors of the true pelvis: Radiological diagnosis for pelvic tumors made simple].

M Meissnitzer, T Meissnitzer, R Forstner

Abstract readEnglish Abstract
PubMed Publisher
In one paragraph

Article in Der Radiologe, 2015. 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 31% 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, 4 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 2 institutions in 2 countries.

M MeissnitzerUniversitätsinstitut für Radiologie, Landeskrankenhaus Salzburg, Paracelsus Medizinische Privatuniversität, Müllner Hauptstraße 48, 5020, Salzburg, Österreich.
T MeissnitzerUniversitätsinstitut für Radiologie, Landeskrankenhaus Salzburg, Paracelsus Medizinische Privatuniversität, Müllner Hauptstraße 48, 5020, Salzburg, Österreich.
R ForstnerUniversitätsinstitut für Radiologie, Landeskrankenhaus Salzburg, Paracelsus Medizinische Privatuniversität, Müllner Hauptstraße 48, 5020, Salzburg, Österreich. R.Forstner@salk.at.
Salzburger Landeskliniken · ATParacelsus Medizinische Privatuniversität · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumors of the reproductive organs are among the most common female tumors. Computed tomography (CT) and magnetic resonance imaging (MRI) are used for establishing the differential diagnosis, for staging and increasingly for treatment stratification. Sonography is the first line imaging modality for assessing benign and malignant tumors of the uterus as well as ovarian and adnexal lesions. In sonographically indeterminate masses MRI is used for clarification as it allows not only a prediction of the dignity but also in most cases a specific diagnosis. The MRI examination is also superior to CT in the assessment of tumors resembling benign pelvic lesions. Whereas MRI has become established as the modality of choice for local staging and treatment planning in cancers of the uterine cervix and endometrial cancer, CT is used for staging of ovarian cancer due to the propensity for peritoneal spread outside the pelvis at the time of initial diagnosis.

Indexed as

Evidence-Based MedicineFemaleGenital Neoplasms, FemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingPelvic NeoplasmsReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray ComputedDiagnostic imagingDifferential diagnosisOvarian neoplasmsPelvic neoplasmsUltrasonography

Identifiers

PMID26610683
OpenAlexW2685326676

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.