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Record W2343123075 · doi:10.1002/pmh.1337

The clinical trajectory of patients with borderline personality disorder

2016· article· en· W2343123075 on OpenAlexaff
Jesper Kjær, Robert Biskin, Claus Høstrup Vestergaard, Lea Nørgreen Gustafsson, Povl Munk‐Jørgensen

Bibliographic record

VenuePersonality and Mental Health · 2016
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill University
Fundersnot available
KeywordsBorderline personality disorderPsychologyPsychotherapistClinical psychologyPersonalityTrajectoryPsychoanalysis

Abstract

fetched live from OpenAlex

OBJECTIVE: The epidemiological data on the diagnostic course of patients with borderline personality disorder (BPD) is limited. We used a nationwide register to investigate the diagnostic stability and changes over time. METHOD: The Danish nationwide registers were used to follow all patients with a diagnosis of BPD and investigate their first-ever psychiatric diagnosis and their latest diagnosis in the time period of 1995-2012. From this, we found the diagnostic stability and described the diagnostic changes. RESULTS: A total of 10 786 patients diagnosed with BPD were identified. The prospective diagnostic stabilities were 37% for females and 25% for males, and retrospective stabilities were 20% for females and 22% for males. More than 60% of patients received other diagnoses than BPD as their first-ever diagnosis. Stress-related (17%) and depressive disorders (14%) were most frequent as first-ever diagnosis. The latest diagnosis remained BPD in nearly half of females and one third of males, followed by schizophrenia, notably for those with longer follow-up and males. CONCLUSION: This study gives a detailed display of complicated clinical trajectories. The low diagnostic stabilities demonstrate a heterogenous patient group diagnosed with many other psychiatric diagnoses over time. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.002
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 score0.128
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.380
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 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

Citations26
Published2016
Admission routes1
Has abstractyes

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