Socio-demographic and Clinical Correlates of Facial Expression Recognition Disorder in the Euthymic Phase of Bipolar Patients
Bibliographic record
Abstract
OBJECTIVE: Bipolar patients show social cognitive disorders. The objective of this study is to review facial expression recognition (FER) disorders in bipolar patients (BP) and explore clinical heterogeneity factors that could affect them in the euthymic phase: socio-demographic level, clinical and changing characteristics of the disorder, history of suicide attempt, and abuse. METHOD: Thirty-four euthymic bipolar patients and 29 control subjects completed a computer task of explicit facial expression recognition and were clinically evaluated. RESULTS: Compared with control subjects, BP patients show: a decrease in fear, anger, and disgust recognition; an extended reaction time for disgust, surprise and neutrality recognition; confusion between fear and surprise, anger and disgust, disgust and sadness, sadness and neutrality. In BP patients, age negatively affects anger and neutrality recognition, as opposed to education level which positively affects recognizing these emotions. The history of patient abuse negatively affects surprise and disgust recognition, and the number of suicide attempts negatively affects disgust and anger recognition. CONCLUSIONS: Cognitive heterogeneity in euthymic phase BP patients is affected by several factors inherent to bipolar disorder complexity that should be considered in social cognition study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".