MétaCan
Menu
Back to cohort
Record W2895647406 · doi:10.12740/app/94398

Therapeutic difficulties in management of the patient with anorexia nervosa and comorbid borderline personality disorder – case study

2018· article· en· W2895647406 on OpenAlexaboutno aff
Katarzyna Kordyńska, Barbara Kostecka, Paweł Sala, Katarzyna Kucharska

Bibliographic record

VenueArchives of Psychiatry and Psychotherapy · 2018
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsPsychopathologyAnorexia nervosaPsychologyBorderline personality disorderAlexithymiaClinical psychologyBeck Depression InventoryPsychiatryEating disordersAnxietyPsychological interventionDepression (economics)Psychotherapist

Abstract

fetched live from OpenAlex

Aim: This case report addresses some clinical challenges occurring throughout the therapeutic process of specialized management treatment of Anorexia Nervosa with comorbid Borderline Personality Disorder. Materials and method: Clinical examinations and measures – Structured Clinical Interview for DSM-IV (SCID-II), Eating Attitudes Test (EAT-26), Yale-Brown Obsessive Compulsive Scale modified for Body Dysmorphic Disorder (BDD-YBOCS), Beck Depression Inventory-II (BDI-II), Toronto Alexithymia Scale (TAS-20), and State-Trait Anxiety Inventory (STAI) have been employed to identify the nature and severity of patient’s psychopathology and its potential change throughout the therapeutic process. Results: Despite some improvement in the symptoms of the eating disorder following the treatment, some other measures of psychopathology (e.g., depression) remained at similar level. Conclusions: Only partial improvement in the profile of F.C.’s psychopathology was observed. Discussion: Management of AN with comorbid BPD requires a comprehensive care package including integrative psychotherapy, nutritional interventions and pharmacotherapy at an inpatient setting as well as follow-up care thereafter.

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)0.000
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 score0.032
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueArchives of Psychiatry and PsychotherapySame topicEating Disorders and BehaviorsFrench-language works237,207