Abnormalities of emotional awareness and perception in patients with obsessive-compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Emotional awareness deficit may play a critical role in the production and maintenance of obsessive-compulsive symptoms and social dysfunction in patients with obsessive-compulsive disorder (OCD). The aim of this study was to investigate characteristics of emotional awareness such as empathy and alexithymia in OCD patients. In addition, we examined whether impaired emotional awareness measured by self-assessment questionnaires was associated with emotional facial recognition ability in OCD patients.\n\nMETHODS: Study participants included 107 patients with OCD and 130 healthy age- and sex-matched volunteers. The Interpersonal Reactivity Index (IRI) and Toronto Alexithymia Scale-20 were applied as measures of empathy and alexithymia. A subset of 56 patients with OCD additionally performed the emotional perception task of face expression.\n\nRESULTS: Patients with OCD scored significantly lower for perspective taking, and significantly higher for personal distress of IRI, and significantly higher for alexithymia compared to normal controls. Impaired emotional awareness such as lower perspective taking and fantasy seeking had a perception bias towards disgust in response to ambiguous facial expressions in OCD patients.\n\nLIMITATIONS: The OCD group consisted of patients in different stages of the illness and with different degrees of severity.\n\nCONCLUSIONS: OCD involves the impairment of emotional awareness and perception and it may relate to social dysfunction and to impairments in the ability to shift naturally from obsessive thoughts to other thoughts in response to social situations in patients with OCD.
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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 | 0.000 | 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".