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Record W2892992963 · doi:10.5334/pb.420

Different Clinical Presentations in Eating Disorder Patients with Non-Suicidal Self-Injury Based on the Co-Occurrence of Borderline Personality Disorder

2018· article· en· W2892992963 on OpenAlexaff
Laurence Claes, Brianna J. Turner, Eva Dierckx, Koen Luyckx, Margaux Verschueren, Katrien Schoevaerts

Bibliographic record

VenuePsychologica Belgica · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBorderline personality disorderPsychologySuicidal ideationClinical psychologyPsychopathologyPsychiatryEating disordersInjury preventionPoison controlMedicineMedical emergency

Abstract

fetched live from OpenAlex

Non-suicidal self-injury (NSSI) and borderline personality disorder (BPD) features are common in patients with eating disorders (ED), yet little is known regarding the clinical presentation of ED patients who present with NSSI with and without BPD. The current study compared self-injurious, female ED inpatients with (n = 98; NSSI+BPD) and without BPD (n = 45; NSSI-only) on different self-reported clinical features. Results suggest that ED patients with NSSI+BPD differ from those with NSSI-only with regard to frequency of suicidal ideation, alcohol, drug or medication abuse, internalizing/externalizing psychopathology, interpersonal problems, and coping strategies, with the NSSI+BPD group demonstrating more impairment in each of these domains. Despite these differences in clinical presentation, however, groups did not differ in NSSI features. In sum, while self-injurious ED patients may present with similar NSSI behavior regardless of BPD diagnosis, those with NSS+BPD represent a group with much higher clinical complexity and greater treatment needs.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.001
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 head0.050
GPT teacher head0.395
Teacher spread0.344 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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