Evidence map›Paper›PMID 36451770›Full record

ArticleFrontiers in psychiatry2022

Invariance-based causal prediction to identify the direct causes of suicidal behavior.

Austin V Goddard, Yu Xiang, Craig J Bryan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. 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
0.8field-weighted citation impact, top 27% 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

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, 4 citations in OpenAlex.

  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

3 authors at 2 institutions in 1 country.

Austin V GoddardDepartment of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, United States.
Yu XiangDepartment of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, United States.
Craig J BryanDepartment of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, United States.
University of Utah · USThe Ohio State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite decades of research, the direct causes of suicide remain unknown. Some researchers have proposed that suicide is sufficiently complex that no single variable or set of variables can be determined causal. The invariance-based causal prediction (ICP) is a contemporary data analytic method developed to identify the direct causal relationships, but the method has not yet been applied to suicide. In this study, we used ICP to identify the variables that were most directly related to the emergence of suicidal behavior in a prospective sample of 2,744 primary care patients. Fifty-eight (2.1%) participants reported suicidal behavior during the following year. Of 18 predictors tested, shame was most likely to be directly causal only under the least restrictive conditions. No single variable or set of variables was identified. Results support the indeterminacy hypothesis that suicide is caused by many combinations of factors, none of which are necessary for suicide to occur.

Indexed as

causal predictioninvariancemilitaryprimary care (MeSH)suicide

Identifiers

PMID36451770
PMCPMC9701748
OpenAlexW4309043389

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.