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Record W2997916608 · doi:10.1521/pedi_2019_33_461

Impact of Childhood Maltreatment in Borderline Personality Disorder on Treatment Response to Intensive Dialectical Behavior Therapy

2019· article· en· W2997916608 on OpenAlexaff
Sebastian Euler, Esther Stalujanis, Hannah J. Lindenmeyer, Rosetta Nicastro, Uëli Kramer, Nader Perroud, Sébastien Weibel

Bibliographic record

VenueJournal of Personality Disorders · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsDalhousie UniversityUniversity of Windsor
Fundersnot available
KeywordsBorderline personality disorderImpulsivityPsychologyPsychological abuseDialectical behavior therapySexual abuseClinical psychologyNeglectPhysical abuseChild abusePsychiatryPoison controlInjury preventionMedicine

Abstract

fetched live from OpenAlex

Childhood maltreatment (CM), including emotional, physical, and sexual abuse and emotional and physical neglect, is associated with severity of borderline personality disorder (BPD). However, knowledge on the impact of CM on treatment response is scarce. The authors investigated whether self-reported CM or one of its subtypes affected treatment retention, depressive symptoms, and impulsivity throughout short-term intensive dialectical behavior therapy (I-DBT) in 333 patients with BPD. Data were analyzed with linear and logistic regressions and linear mixed models, using a Bayesian approach. Patients who reported childhood emotional abuse had a higher dropout rate, whereas it was lower in patients who reported childhood emotional neglect. Emotional neglect predicted a greater decrease of depressive symptoms, and global CM predicted a greater decrease of impulsivity. The authors concluded that patients with BPD who experienced CM might benefit from I-DBT in specific symptom domains. Nonetheless, the impact of emotional abuse on higher dropout needs to be considered.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.374
Teacher spread0.348 · 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

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