Evidence map›Paper›PMID 41693468›Full record

ArticleBJPsych open2026

Little information, great impact? A clinical tool for the prediction of electroconvulsive therapy effectiveness in depression.

Michael Belz, Isabel Methfessel, Matthias Besse, Melvin Heinisch, Wolfgang Strube, Joshua Tritsch, Alkomiet Hasan, David Zilles-Wegner

Abstract read
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In one paragraph

Article in BJPsych open, 2026. 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. 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

8 authors.

Michael BelzDepartment of Psychiatry and Psychotherapy, https://ror.org/021ft0n22University Medical Center Göttingen, D-Göttingen, Germany.ORCID https://orcid.org/0000-0001-9250-6049
Isabel MethfesselDepartment of Psychiatry and Psychotherapy, https://ror.org/021ft0n22University Medical Center Göttingen, D-Göttingen, Germany.
Matthias BesseDepartment of Psychiatry and Psychotherapy, https://ror.org/021ft0n22University Medical Center Göttingen, D-Göttingen, Germany.
Melvin HeinischDepartment of Psychiatry and Psychotherapy, https://ror.org/021ft0n22University Medical Center Göttingen, D-Göttingen, Germany.
Wolfgang StrubeDepartment of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, Augsburg, Germany.
Joshua TritschDepartment of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, Augsburg, Germany.
Alkomiet HasanDepartment of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, Augsburg, Germany.
David Zilles-WegnerDepartment of Psychiatry and Psychotherapy, https://ror.org/021ft0n22University Medical Center Göttingen, D-Göttingen, Germany.ORCID https://orcid.org/0000-0002-7531-9732

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe effectiveness of electroconvulsive therapy (ECT) for depression strongly depends on patient characteristics. Clinical factors may increase (e.g. age, psychotic symptoms) or decrease (e.g. episode duration) response rates.

aimsThis prospective study aimed to develop an instrument for the prediction of ECT response in patients with unipolar depression.

method

resultsResults showed a high correlation between GREAT-score and dichotomous response (

conclusionsOur prospective study shows that an instrument consisting of seven clinical items is able to predict ECT response in depression with good accuracy. Limitations include a relatively small sample size and the lack of further potential predictors suggested by recent studies. GREAT will thus be modified to further improve its accuracy. Currently, it may give clinicians a relevant estimate of the likelihood and the extent of the individual response to ECT.

Indexed as

depressive disorderElectroconvulsive therapy (ECT)response predictiontreatment outcome

Identifiers

PMID41693468
PMCPMC12926877

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Registered trials

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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.