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Record W4300235084

Persistent Suicide Risk in Clinically Improved Schizophrenia Patients: Challenge of the Suicidal Dimension

2010· article· en· W4300235084 on OpenAlexaboutno aff
Amresh Shrivastava, Megan Johnston, Nilesh Shah

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2010
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychiatryDimension (graph theory)PsychologySuicide RiskMedicineClinical psychologySuicide preventionMedical emergencyPoison controlMathematics
DOInot available

Abstract

fetched live from OpenAlex

Amresh Shrivastava1, Megan E Johnston2, Nilesh Shah3, Marco Innamorati4, Larry Stitt5, Meghana Thakar3, David Lester6, Maurizio Pompili4,71Silver Mind Hospital and Mental Health Foundation of India, Mumbai, India; 2Department of Psychology, University of Toronto, Toronto, ON, Canada; 3Lokmanya Tilak Municipal General Hospital, University of Mumbai, India; 4Department of Neurosciences, Mental Health and Sensory Functions, Suicide Prevention Center, Sant’Andrea Hospital, Sapienza University of Rome, Rome, Italy; 5Department of Biostatistics, The University of Western Ontario, London, ON, Canada; 6The Richard Stockton College of New Jersey, Pomona, NJ, USA; 7McLean Hospital, Harvard Medical School, Boston, MA, USABackground: Suicide is a major problem in schizophrenia, estimated to affect 9%–13% of patients. About 25% of schizophrenic patients make at least one suicide attempt in their lifetime. Current outcome measures do not address this problem, even though it affects quality of life and patient safety. The aim of this study was to assess suicidality in long-term clinically improved schizophrenia patients who were treated in a nongovernmental psychiatric treatment centre in Mumbai, India.Method: Participants were 61 patients out of 200 consecutive hospitalized first-episode patients with schizophrenia diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders who were much improved on the Clinical Global Impression Scale-Improvement (CGI-I) scale at the endpoint of a 10-year follow-up. Clinical assessment tools included the Positive and Negative Syndrome Scale for Schizophrenia, CGI-I, Global Assessment of Functioning, and suicidality.Results: Many of the patients, although clinically improved, experienced emerging suicidality during the 10-year follow-up period. All of the patients reported significant suicidality (ie, suicide attempts, suicidal crises, or suicidal ideation) at the end of the study, whereas only 83% had reported previous significant suicidality at baseline. No sociodemographic and clinical variables at baseline were predictive of suicidal status at the end of the 10-year follow-up.Conclusion: Schizophrenia is a complex neurobehavioral disorder that appears to be closely associated with suicidal behavior. Adequate assessment and management of suicidality needs to be a continual process, even in patients who respond well to treatment.Keywords: schizophrenia, suicide risk, prevention

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.130
GPT teacher head0.496
Teacher spread0.366 · 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".

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Citations2
Published2010
Admission routes1
Has abstractyes

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