Evidence map›Paper›PMID 30389985›Full record

ArticleScientific reports2018

Mining latent information in PTSD psychometrics with fuzziness for effective diagnoses.

Yuanyuan Li, Xi Xiong, Changjian Qiu, Qiang Wang, Jiajun Xu

Abstract read
In one paragraph

Article in Scientific reports, 2018. 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

5 authors.

Yuanyuan LiMental Health Center, West China Hospital of Sichuan University, Chengdu, 610041, China.
Xi XiongSchool of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China. xiongxi@cuit.edu.cn.
Changjian QiuMental Health Center, West China Hospital of Sichuan University, Chengdu, 610041, China. qiuchangjian18@163.com.ORCID http://orcid.org/0000-0002-1906-2530
Qiang WangMental Health Center, West China Hospital of Sichuan University, Chengdu, 610041, China.
Jiajun XuMental Health Center, West China Hospital of Sichuan University, Chengdu, 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The options of traditional self-report rating-scale, like the PTSD Checklist Civilian (PCL-C) scale, have no clear boundaries which might cause considerable biases and low effectiveness. This research aimed to explore the feasibility of using fuzzy set in the data processing to promote the screening effectiveness of PCL-C in real-life practical settings. The sensitivity, specificity, Youden's index etc., of PCL-C at different cutoff lines (38, 44 and 50 respectively) were analyzed and compared with those of fuzzy set approach processing. In practice, no matter the cutoff line of the PCL-C was set at 50, 44 or 38, the PCL-C showed good specificity, but failed to exhibit good sensitivity and screening effectiveness. The highest sensitivity was at 65.22%, with Youden's index being 0.64. After fuzzy processing, the fuzzy-PCL-C's sensitivity increased to 91.30%, Youden's index rose to 0.91, having seen marked augmentation. In conclusion, this study indicates that fuzzy set can be used in the data processing of psychiatric scales which have no clear definition standard of the options to improve the effectiveness of the scales.

Identifiers

PMID30389985
PMCPMC6214927

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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.