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Record W2952959089 · doi:10.1002/jclp.22817

Psychiatric disorders among offspring of patients with Bipolar and Borderline Personality Disorder

2019· article· en· W2952959089 on OpenAlexaff
Anne‐Lise Küng, Eléonore Pham, Paolo Cordera, Roland Hasler, Jean‐Michel Aubry, Alexandre Dayer, Nader Perroud, Camille Piguet

Bibliographic record

VenueJournal of Clinical Psychology · 2019
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
FundersNational Center of Competence in Research Chemical BiologySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsOffspringBorderline personality disorderBipolar disorderPsychologyPsychiatryPrevalence of mental disordersPersonality disordersClinical psychologyPersonalityMental healthPregnancyCognition

Abstract

fetched live from OpenAlex

OBJECTIVE: As part of a larger study investigating biological risk factors for bipolar disorder (BD) and borderline personality disorder (BPD), we investigated the prevalence of psychiatric diagnoses presented by young BD or BPD offspring. With respect to the scarcity of studies interested in psychiatric disorders among BPD offspring, we have chosen to report these results despite the small sample size for a prevalence study. METHOD: We recruited 21 BD and 22 BPD offspring and 23 control subjects. All subjects were assessed with a structured interview. RESULTS: Our main finding suggests that BPD offspring present a higher rate of psychiatric disorders compared to BD offspring. Attention deficit and hyperactivity disorder was the most prevalent disorder. CONCLUSION: Our results contribute to the evidence that offspring of patients with BPD, are at high risk with regard to their mental health and deserve both more research and special attention at the clinical level.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.032
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.377
Teacher spread0.353 · 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 teacher head, 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

Citations9
Published2019
Admission routes1
Has abstractyes

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