Trajectories, comorbidity, and risk factors for adolescent disordered eating and borderline personality disorder features
Bibliographic record
Abstract
PURPOSE: Borderline personality disorder (BPD) and eating disorders are highly comorbid, but the shared course of symptoms and associated risks remain poorly understood. The aim of this study was to examine joint symptom trajectories, temporal precedence, risk factors, and population attributable fractions (PAFs) in a community sample of adolescents, using a developmental psychopathology and psychosocial framework. METHODS: = 544, 56% girls) reported on BPD features and disordered eating behavior. Sociodemographic, interpersonal, and clinical risks were assessed in childhood (age 10-13 years). We used a person-centered approach to examine latent class growth analyses, joint trajectory models, and calculated PAFs. RESULTS: Three-class solutions were found for both disordered eating and BPD features (low, moderate, high), creating nine joint trajectories. High levels of disordered eating were a stronger indicator of high levels of BPD features than was the reverse. Girls and LGBTQ+ youth were most likely to be in a high symptom trajectory. Bullying perpetration and clinical hyperactivity were unique risks for BPD features. Bullying victimization contributed the largest PAF to disordered eating and BPD features. CONCLUSION: We identified several novel and clinically relevant findings related to temporality, risks, screening, and the treatment of adolescent eating problems and BPD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".