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Record W2742659547 · doi:10.1177/2167702617718193

Decreased Neural Response to Threat Differentiates Patients Who Have Attempted Suicide From Nonattempters With Current Ideation

2017· article· en· W2742659547 on OpenAlexaff
Anna Weinberg, Alexis M. May, E. David Klonsky, Roman Kotov, Greg Hajcak

Bibliographic record

VenueClinical Psychological Science · 2017
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsSuicidal ideationPsychologyIdeationSuicide preventionPsychiatrySuicide ideationClinical psychologySuicide attemptPoison controlInjury preventionHuman factors and ergonomicsPsychological painMedicineMedical emergency

Abstract

fetched live from OpenAlex

Suicide prevention efforts have not slowed suicide rates, in part because of limited understanding of differences in risk for suicide ideation versus suicide attempts. Reduced fear of pain and death may be key to this distinction. In the present study we examine whether blunted neural response to threat of death, bodily harm, or illness, measured by the late positive potential (LPP), differentiates individuals who had previously attempted suicide from individuals who had never attempted suicide, controlling for current levels of suicidal ideation. We compared psychiatric outpatients with no history of suicide attempts ( n = 152) and those with a history of suicide attempts ( n = 83). Attempters exhibited a blunted threat-elicited LPP compared to patients with no history of attempts, regardless of current ideation. Findings suggest diminished neural response to threat can distinguish attempters from ideators and might be a target for future research on the transition from ideation to action.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.159
GPT teacher head0.481
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2017
Admission routes1
Has abstractyes

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