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Exploring screening for borderline personality disorder in pediatric inpatients with psychiatric Illness

2022· article· en· W4205723522 on OpenAlexafffund
Michèle Preyde, Marco DiCroce, Shrenik Parekh, John Heintzman

Bibliographic record

VenuePsychiatry Research · 2022
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsGrand River HospitalUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBorderline personality disorderPsychiatryClinical psychologyDistressIntervention (counseling)AnxietyPsychologyMedicine

Abstract

fetched live from OpenAlex

Borderline Personality Disorder (BPD) is a severe psychiatric illness associated with poor personal and interpersonal functioning. Screening for BPD in adolescents and provision of specialized treatment may improve life circumstances in vocations and relationships. The purpose of this study was to determine the number of pediatric inpatients who would screen positive for BPD with a self-rating measure, and to compare their personal and interpersonal characteristics with youth who did not screen positive. A survey with self-report measures was administered to patients to screen for BPD. The mean age of the sample was 15 years and 71% identified as female gender. Of 109 patients 72 (66%) screened positive for BPD while only eight (7%) patients were diagnosed by psychiatrists with BPD or features of BPD. There were no statistically significant differences between those who scored positive versus negative for BPD in age, gender, or avoidant anxiety. There were statistically significant differences in anxious attachment, distress, clinical symptoms, problematic use of electronic devices, considered suicide, past trauma and prior suspensions from school. This exploration in pediatric inpatients suggests that many of these patients may be at risk for a diagnosis of BPD later in life and may benefit from early identification and specialized intervention.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.167
GPT teacher head0.407
Teacher spread0.240 · 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.

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

Citations9
Published2022
Admission routes2
Has abstractyes

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