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Record W2596616928 · doi:10.1017/s1092852917000177

Prevalence of suicide attempt and clinical characteristics of suicide attempters with obsessive-compulsive disorder: a report from the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS)

2017· article· en· W2596616928 on OpenAlexaff
Bernardo Dell’Osso, Beatrice Benatti, C. Arici, Carlotta Palazzo, A. Carlo Altamura, Eric Hollander, Naomi Fineberg, Dan J. Stein, Humberto Nicolini, Nuria Lanzagorta, Donatella Marazziti, Stefano Pallanti, Michael Van Ameringen, Christine Löchner, Oğuz Karamustafalıoğlu, Luchezar Hranov, Martijn Figee, Lynne M. Drummond, Carolyn I. Rodríguez, John Grant, Damiaan Denys, José M. Menchón, Joseph Zohar

Bibliographic record

VenueCNS Spectrums · 2017
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcMaster University
FundersNational Institute of Mental HealthMedical Research CouncilH. Lundbeck A/SServierBrainsWayPsyadon PharmaceuticalsNational Center for Responsible GamingEuropean College of NeuropsychopharmacologyNancy R. Gelman Foundation
KeywordsLogistic regressionMedicinePsychiatrySuicide attemptObsessive compulsiveDemographyInternal medicineDepression (economics)Mini-international neuropsychiatric interviewClinical psychologyPoison controlSuicide preventionAnxietyMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: Obsessive-compulsive disorder (OCD) is associated with variable risk of suicide and prevalence of suicide attempt (SA). The present study aimed to assess the prevalence of SA and associated sociodemographic and clinical features in a large international sample of OCD patients. METHODS: A total of 425 OCD outpatients, recruited through the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS) network, were assessed and categorized in groups with or without a history of SA, and their sociodemographic and clinical features compared through Pearson's chi-squared and t tests. Logistic regression was performed to assess the impact of the collected data on the SA variable. RESULTS: 14.6% of our sample reported at least one SA during their lifetime. Patients with an SA had significantly higher rates of comorbid psychiatric disorders (60 vs. 17%, p<0.001; particularly tic disorder), medical disorders (51 vs. 15%, p<0.001), and previous hospitalizations (62 vs. 11%, p<0.001) than patients with no history of SA. With respect to geographical differences, European and South African patients showed significantly higher rates of SA history (40 and 39%, respectively) compared to North American and Middle-Eastern individuals (13 and 8%, respectively) (χ2=11.4, p<0.001). The logistic regression did not show any statistically significant predictor of SA among selected independent variables. CONCLUSIONS: Our international study found a history of SA prevalence of ~15% in OCD patients, with higher rates of psychiatric and medical comorbidities and previous hospitalizations in patients with a previous SA. Along with potential geographical influences, the presence of the abovementioned features should recommend additional caution in the assessment of suicide risk in OCD 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 score0.002
Threshold uncertainty score0.004

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 integrity0.0000.000
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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

Citations42
Published2017
Admission routes1
Has abstractyes

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