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Record W2621748202 · doi:10.1097/nmd.0000000000000697

The Rate of Improvement in Long-Term Dynamic Psychotherapy For Borderline Personality Disorder

2017· article· en· W2621748202 on OpenAlexaff
J. Christopher Perry, Michael Bond, Vera Békés

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2017
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsJewish General HospitalHEC Montréal
Fundersnot available
KeywordsBorderline personality disorderPsychopathologyPsychologyClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Controlled trials of psychotherapy and follow-up studies of borderline personality disorder (BPD) have shown significant, but usually limited, improvement. We examined the hypothesis that BPD changes more slowly than nonborderline disorders. In a study of long-term dynamic psychotherapy, 16 subjects with BPD and 35 with non-BPD disorders were treated for a median of 3 years with a follow-up of 5 years. From periodic assessments, we calculated the rate of change for each subject over the course of the study on each measure of symptoms and functioning. At intake, borderline psychopathology was associated with higher levels on 76% of 17 measures of comorbid axis I disorders, symptoms, and functioning. BPD psychopathology was associated with faster (not slower) rates of improvement on three measures, but after controlling for the initial level of each measure, there were no significant associations. These findings counsel both optimism and patience in the long-term treatment of patients with BPD.

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)0.004
metaresearch head score (Gemma)0.024
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.355
Teacher spread0.338 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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