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Record W2964125714 · doi:10.1097/nmd.0000000000001023

Association of Psychache and Alexithymia With Suicide in Patients With Schizophrenia

2019· article· en· W2964125714 on OpenAlexaboutno aff
Mehmet Emin Demirkol, Lut Tamam, Zeynep Namlı, Mahmut Onur Karaytuğ, Kerim Uğur

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaSchizophrenia (object-oriented programming)Association (psychology)PsychologyClinical psychologyPsychiatryPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Suicide is a leading cause of death in patients with schizophrenia. Previous studies have mostly investigated the association between suicide and sociodemographics, positive and negative symptoms, and depressive symptoms. This study evaluated psychache and alexithymia in patients with schizophrenia, which have both been associated with suicide attempts and thoughts in patients with other psychiatric disorders. Positive and Negative Syndrome Scale (PANSS), Psychache Scale (PAS), Beck Scale for Suicidal Ideation (BSSI), Calgary Depression Scale for Schizophrenia (CDSS), and Toronto Alexithymia Scale (TAS) scores were obtained in 113 patients with schizophrenia, including 50 with suicide attempts. PANSS positive symptoms and general psychopathology subscale, CDSS, BSSI, TAS, and PAS scores were significantly higher in patients with suicide attempts. In multivariate logistic regression analysis, only the PAS score was an independent predictor of attempted suicide. Mediation analysis demonstrated that psychache (both directly and indirectly) and alexithymia (indirectly) might be associated with the risk of suicide in these patients.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.220
Teacher spread0.215 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

Explore more

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