Eating disorders among patients with borderline personality disorder: understanding the prevalence and psychopathology
Bibliographic record
Abstract
BACKGROUND: Treatment protocols can be bolstered and etiological and maintenance factors can be recognized more easily by a superior understanding of emotions and emotion regulation in the comorbidity of borderline personality disorder (BPD) and feeding and eating disorders (FEDs). Therefore, the present study aimed at investigating the prevalence and psychopathology of FEDs in patients with BPD. METHODS: = 110), from August 2018 to November 2019. The participants were selected by systematic random sampling among the patients who referred to Baharan psychiatric hospital in Zahedan, Iran, with the sampling interval of 3. The subjects were evaluated by 28-item General Health Questionnaire (GHQ-28), Borderline Personality Inventory (BPI), Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD), Structured Clinical Interviews for DSM-5: Research Version (SCID-5-RV), the 26-item Eating Attitudes Test (EAT-26), 20-item Toronto Alexithymia Scale (TAS-20), Beck Anxiety Inventory (BAI), and Beck Depression Inventory-II (BDI-II). RESULTS: = 72) prevalence of FEDs in patients with BPD. Also, the highest and lowest prevalence rates were reported for other specified feeding and eating disorders (51.3%) and bulimia nervosa (6.9%), respectively. Although the highest mean score of TAS-20 was related to anorexia nervosa, there was no significant difference between the scores of various types of FEDs. The mediation analysis showed that anxiety and depression would play a mediating role in the relationship between alexithymia and eating-disordered behaviors. CONCLUSIONS: The results have suggested that alexithymia, anxiety, and depression should receive clinical attention as potential therapeutic targets in the comorbidity of BPD and FEDs. The clinical implications of the research have been conducted to date, and directions for future research have been discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".