29 THE RISK FACTOR ANALYSIS OF COGNITIVE IMPAIRMENT IN PATIENTS WITH FIRST-EPISODE SCHIZOPHRENIA IN VOCATIONAL COLLEGES AND UNIVERSITIES
Notice bibliographique
Résumé
Abstract Background Schizophrenia is a complex and severe mental disorder that typically manifests in late adolescence or early adulthood. Its characteristics include abnormalities in thinking, emotions, and behavior, which have long-term adverse effects on the patient’s cognitive and social functioning. Cognitive impairment is one of the important features of schizophrenia, especially in patients with first onset schizophrenia (FES), where this impairment may be more pronounced and directly affect the treatment effectiveness and quality of life of the disease. Vocational college students, as a special group, may experience cognitive impairment due to their educational environment and psychological pressure. Early intervention in FES patients is key to improving long-term prognosis, therefore, in-depth research on the risk factors of cognitive impairment in this population is of great significance for optimizing intervention measures. Methods The study focuses on FES patients in vocational colleges and systematically analyzes the main risk factors for cognitive impairment, providing scientific basis for intervention strategies in this field. The study was designed as a cross-sectional study, recruiting a total of 150 first-episode schizophrenia patients from vocational colleges. Evaluate the overall cognitive function of patients using the Montreal Cognitive Assessment Scale (MoCA), while selecting neuropsychological domains such as memory, executive function, and attention for in-depth analysis. The socio demographic characteristics, disease history, and psychosocial factors of patients were obtained through standardized questionnaires and structured interviews. Using univariate analysis to screen potential variables associated with cognitive impairment, and further determining significant independent risk factors through multivariate logistic regression analysis. Results The analysis of the situation of each patient is shown in Table 1. According to Table 1, among 150 patients, 92 (61.3%) had significant cognitive impairment (MoCA score<26). Multivariate logistic regression analysis showed that the following risk factors were significantly associated with cognitive impairment. Untreated duration of mental illness exceeding 12 months significantly increases the risk of cognitive impairment, and patients with lower levels of education have poorer cognitive function. Patients with higher scores on the Negative Symptom Assessment Scale have a significantly increased risk of cognitive impairment. Discussion The research results indicate that the incidence of cognitive impairment is higher in first-episode schizophrenia patients in vocational colleges, and the main risk factors include longer duration of untreated mental illness, lower education level, and severe negative symptoms. Early intervention should focus on shortening treatment delays, strengthening educational support, and actively treating negative symptoms to alleviate cognitive impairment in patients. Future research can further validate research conclusions through longitudinal studies and explore personalized intervention strategies targeting risk factors to promote patient functional recovery and social integration.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».