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Record W2487351377 · doi:10.1097/pra.0000000000000167

The Effect of Borderline Personality Pathology on Outcome of Cognitive Behavior Therapy

2016· review· en· W2487351377 on OpenAlexaff
Eric Lis, Gail Myhr

Bibliographic record

VenueJournal of Psychiatric Practice · 2016
Typereview
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPersonality pathologyBorderline personality disorderPersonality disordersClinical psychologyPersonalityDepression (economics)AnxietyPsychologyIntervention (counseling)Cognitive behavioral therapyCognitive therapyCognitionPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Cognitive behavioral therapy (CBT) is an evidence-based psychotherapeutic approach which has been shown to be an effective intervention for most psychiatric disorders. There are conflicting data in the literature regarding whether a comorbid personality disorder worsens the prognosis of CBT for depression, anxiety, and other complaints. This study examined data collected before and after courses of CBT for patients with significant borderline (n=39, 11.5%) or obsessive-compulsive (n=66, 19.4%) personality pathology or no personality disorder (n=235, 69.1%). A diagnosis of personality pathology was not a significant predictor of outcome in CBT as measured by the reliable change index. However, patients with borderline personality pathology did demonstrate a greater response to CBT than other patients in terms of improvement on several measures of symptoms. Patients with borderline personality pathology appear to enter therapy with greater subjective depression and interpersonal difficulty than other patients but achieve larger gains during therapy. Implications and directions for future research are discussed.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.055
GPT teacher head0.458
Teacher spread0.403 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2016
Admission routes1
Has abstractyes

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