EPA-0777 – Alexithymia, facial emotion identification and social inference in ed patients: a case-control study
Bibliographic record
Abstract
Introduction Alexythimia, reduced cognitive empathy and emotion awareness and understanding are present among individuals with Eating Disorders (EDs). Facial expression is a reliable marker of emotion and an important source of social information. Thus, the ability to judge facial expression is essential for successful interpersonal interactions. Objectives To evaluate alexythimia, facial emotion identification and social inference abilities in a sample of ED patients, compared to a sample of patients with another psychiatric diagnosis and a group of healthy controls, matched by gender and age. Aims To describe a specific pattern of emotional dysregulation in ED patients. Methods ED patients and the Psychiatric Control Group are recruited at the Institute of Psychiatry in Novara, while healthy controls are recruited on a community basis. All patients and controls are females, aged 18–65. All patients are undergoing the Structured Clinical Interview for DSM-IV -Patient version (SCID-I-P), healthy controls are administered the Structured Clinical Interview for DSM-IV – Non Patient version (SCID-I-NP). All subjects are undergoing the following: SCID-II, Eating Disorder Inventory − 3 (EDI-3), Binge Eating Scale (BES), Beck Depression Inventory (BDI), Symptom Checklist − 90 (SCL-90), Facial Emotion Identification Test (FEIT), The Awareness of Social Inference Test (TASIT), Temperament and Character Inventory (TCI), Rosenberg Self-Esteem Scale (RSES), Interpersonal Reactivity Index (IRI), Toronto Alexithymia Scale (TAS-20). Results The recruitment and analysis of the samples are ongoing. The ED sample is expected to show greater alexythimia and a poorer performance at FEIT and TASIT, compared to the control samples. Conclusions Clinical implications will be 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".