Evidence map›Paper›PMID 35401072›Full record

ArticleFrontiers in neuroscience2022

Juxtaposing Medical Centers Using Different Questionnaires Through Score Predictors.

Clara Puga, Miro Schleicher, Uli Niemann, Vishnu Unnikrishnan, Benjamin Boecking, Petra Brueggemann, Jorge Simoes, Berthold Langguth, Winfried Schlee, Birgit Mazurek and 1 more

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. 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
–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

0 citing papers in PubMed.

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

11 authors.

Clara PugaKnowledge Management & Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Miro SchleicherKnowledge Management & Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Uli NiemannKnowledge Management & Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Vishnu UnnikrishnanKnowledge Management & Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Benjamin BoeckingTinnitus Center, Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Petra BrueggemannTinnitus Center, Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Jorge SimoesDepartment of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
Berthold LangguthDepartment of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
Winfried SchleeDepartment of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
Birgit MazurekTinnitus Center, Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Myra SpiliopoulouKnowledge Management & Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic tinnitus is a clinically multidimensional phenomenon that entails audiological, psychological and somatosensory components. Previous research has demonstrated age and female gender as potential risk factors, although studies to this regard are heterogeneous. Moreover, whilst recent research has begun to identify clinical "phenotypes," little is known about differences in patient population profiles at geographically separated and specialized treatment centers. Identifying such differences might prevent potential biases in joint randomized controlled trials (RCTs) and allow for population-specific treatment adaptations. Method: Two German tinnitus treatment centers were compared regarding pre-treatment data distributions of their patient population bases. To identify overlapping as well as center-specific factors, juxtaposition-, similarity-, and meta-data-based methods were applied. Results: Between centers, significant differences emerged. One center demonstrated some predictive power of the patients of the other center with regard to questionnaire score after treatment, indicating similarities in treatment response across center populations. Furthermore, adherence to the completion of the questionnaires was found to be an important factor in predicting post-treatment data. Discussion: Differential age and gender distributions per center should be considered as regards RCT design and individualized treatment planning.

Indexed as

adherencenetworkspredictive modelingsimilaritysocio-demographicstinnitus

Identifiers

PMID35401072
PMCPMC8984251

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.