Evidence map›Paper›PMID 31623396›Full record

ArticleJournal of clinical medicine2019

Multimodal Treatment of Patients with Mental Symptom Load: A Pre-Post Comparison.

Dieter Melchart, Volker Fischer, Jingzhang Dai, Stefan Hager, Lisa Dersch, Beatrice E Bachmeier

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2019. 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

6 authors.

Dieter MelchartCompetence Centre for Complementary Medicine and Naturopathy (CoCoNat), Technical University of Munich, 80801 Munich, Germany. Dieter.melchart@tum.de.ORCID 0000-0001-9418-9761
Volker FischerTCM Hospital, 93444 Bad Kötzting, Germany. v.fischer@tcm.info.
Jingzhang DaiTCM Hospital, 93444 Bad Kötzting, Germany. jingzhangdai@hotmail.com.
Stefan HagerTCM Hospital, 93444 Bad Kötzting, Germany. s.hager@tcm.info.
Lisa DerschCompetence Centre for Complementary Medicine and Naturopathy (CoCoNat), Technical University of Munich, 80801 Munich, Germany. lisa.dersch@tum.de.
Beatrice E BachmeierCompetence Centre for Complementary Medicine and Naturopathy (CoCoNat), Technical University of Munich, 80801 Munich, Germany. Beatrice.bachmeier@tum.de.ORCID 0000-0002-3592-7354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Traditional Chinese Medicine (TCM) Hospital in Bad Kötzting, Germany, is treating chronically ill patients, covering a broad range of indications. The aim of this study was to prove the efficacy of a multimodal intervention combining mainstream medicine with TCM treatments on the severity of psychopathological symptoms. Out of 966 patients with chronic psychosomatic disease treated 2017 at the TCM Hospital, we selected 759 patients according to specific criteria and analyzed the outcomes after multimodal intervention. The patients completed a validated questionnaire (International Statistical Classification of Diseases (ICD) Symptom-Rating-(ISR)) at admission, discharge, and follow-up. The most frequent ICD-10 diagnoses were "diseases of the musculoskeletal system and connective tissue" (28.5%), "mental and behavioral disorders" (23.7%), and "diseases of the nervous system" (13.8%). Regarding ISR symptom load, "depressive syndrome" and "anxiety syndrome" were the leading burdens showing remissions of about 40%-60% with moderate (0.588) to strong (1.115) effect sizes (Cohen's

Indexed as

ICD-10 symptom rating (ISR)mental symptom loadmultimodal treatmentprospective observational studypsychosomatic disordersTraditional Chinese Medicine (TCM)

Identifiers

PMID31623396
PMCPMC6832432

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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.