Evidence map›Paper›PMID 37139322›Full record

ArticleFrontiers in psychiatry2023

Optimal cutoff scores of the Chinese version of 15-item negative symptom assessment that indicate prominent negative symptoms of schizophrenia.

Qi Zhou, Cheng-Cheng Pu, Bing-Jie Huang, Qi Miao, Tian-Hang Zhou, Zhang Cheng, Tian-Qi Gao, Chuan Shi, Xin Yu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2023. 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, top 92% 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

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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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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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

9 authors at 4 institutions in 1 country.

Qi ZhouPeking University Sixth Hospital, Beijing, China.
Cheng-Cheng PuPeking University Sixth Hospital, Beijing, China.
Bing-Jie HuangPeking University Sixth Hospital, Beijing, China.
Qi MiaoShandong Mental Health Center, Shandong University, Jinan, China.
Tian-Hang ZhouPeking University Sixth Hospital, Beijing, China.
Zhang ChengPeking University Sixth Hospital, Beijing, China.
Tian-Qi GaoPeking University Sixth Hospital, Beijing, China.
Chuan ShiPeking University Sixth Hospital, Beijing, China.
Xin YuPeking University Sixth Hospital, Beijing, China.
Peking University Sixth Hospital · CNNational Health and Family Planning Commission · CNPeking University · CNShandong Mental Health Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The Chinese version of 15-item negative symptom assessment (NSA-15) is an instrument with a three-factor structure specifically validated for assessing negative symptoms of schizophrenia. To provide a reference for future practical applications in the recognition of schizophrenia patients with negative symptoms, this study aimed to determine an appropriate NSA-15 cutoff score regarding negative symptoms to identify prominent negative symptoms (PNS). Methods: A total of 199 participants with schizophrenia were recruited and divided into the PNS group ( Results: The optimal cutoff NSA-15 score for identifying PNS was 40. Communication, emotion and motivation factors in the NSA-15 had cutoffs of 13, 6, and 16, respectively. The communication factor score had slightly better discrimination than scores on the other two factors. The discriminant ability of the global rating of the NSA-15 was not as good as that of the NSA-15 total score (area under the curve (AUC): 0.873 vs. 0.944). Conclusion: The optimal NSA-15 cutoff scores for identifying PNS in schizophrenia were determined in this study. The NSA-15 provides a convenient and easy-to-use assessment for identifying patients with PNS in Chinese clinical situations. The communication factor of the NSA-15 also has excellent discrimination.

Indexed as

15-item negative symptom assessment (NSA-15)cutoff scoreprominent negative symptomsreceiver operating characteristic curveschizophrenia

Identifiers

PMID37139322
PMCPMC10149848
OpenAlexW4366210623

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LicenceCC BY
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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.