Eating disorders among borderline patients: understanding the prevalence and psychopathology
Bibliographic record
Abstract
Abstract Background: Common comorbidity and the shared psychopathology in borderline personality disorder (BPD) and feeding and eating disorder (FED) resulted in conceptualization of the relationship theory between disordered eating behaviors (DEB), alexithymia, depression, and anxiety. Therefore, the present study aims at investigating the FED prevalence in patients with BPD and evaluating the relationship between DEB, alexithymia, anxiety, and depression.Methods: This cross-sectional study was performed from August 2018 to November 2019; 110 patients with BPD and 110 healthy people were studied in this research. The participants were selected by systematic random sampling out of the patients referring to Baharan psychiatric hospital in Zahedan, Iran, with the sampling interval of 3. The subjects were evaluated by demographic data form, the 26-item eating attitudes test (EAT-26), 20-item Toronto alexithymia scale (TAS-20), Beck anxiety disorder (BAI), and Beck depression inventory-II (BDI-II).Results: The results show a 65.4% (n = 72) prevalence of FED in borderline patients; the highest and lowest prevalence rates are reported for avoidant/restrictive food intake disorder (ARFID) and bulimia nervosa, respectively. The highest mean score of TAS-20 is reported in anorexia nervosa. The regression analysis results show that anxiety and depression play a mediating role in the relationship between alexithymia and DEB.Conclusions: The results suggest that alexithymia should be paid clinical attention as a trait and distress-independent construct in the BPD and FED comorbidity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".