OBJECTIVE: There is a need for models that predict accurately the @entity146 . METHOD: Eating-disordered female inpatients were assessed longitudinally at the beginning of treatment (t1), at the end of treatment (t2), at 2 or 3-year follow-up (t3), and at 6-year follow-up (t4). The sample consisted of 196 @entity1 with @entity1279 ( @entity1279 ) purging type, 103 @entity1 with @entity1278 ( @entity1278 ), and 68 @entity1 with @entity5550 ( @entity5550 ; N=367). Confirmatory factor analysis and path analysis were used to predict the @entity1 's status at 6-year follow-up. RESULTS: The results for @entity1279 and @entity5550 show that the specific @entity1277 pathology was influenced mainly by specific @entity1277 pathology at earlier time points and not by non-eating-specific (general) psychopathology. Similarly, general psychopathology was influenced mainly by general psychopathology at earlier time points. For @entity1278 @entity1 , both categories of psychopathology (eating specific and general) were relevant for the 6-year outcome. The potential impact of 14 factors on the level of pathology was estimated (a) at baseline (at the beginning of treatment), (b) during the @entity221 (baseline controlled), and (c) on the 6-year outcome of @entity1277 (baseline and course controlled). Although there were many correlations between potential factors and baseline pathology, there was only a limited number of significant correlations with the 6-year outcome. This effect was mediated largely by the level of general psychopathology. DISCUSSION: The models for outcome prediction based on structural equation modeling techniques were very similar for @entity1279 and @entity5550 . For both @entity1279 and @entity5550 , there were almost entirely separate predictions for the specific @entity1277 on the one hand and non-eating-related (general) psychopathology on the other hand. This was true to a lesser degree for @entity1278 . CONCLUSIONS: The use of refined path analytic methods in follow-up studies on larger general populations will be helpful to increase our understanding of the @entity221 of @entity146 .

Predicting the outcome of XXXX using structural equation modeling.

@entity1 :: ('9606', 'Species') :: ['women', 'patients'] @entity146 :: ('MESH:D001523', 'Disease') :: ['psychiatric disorders', 'course of mental disorders'] @entity1278 :: ('MESH:D000856', 'Disease') :: ['anorexia nervosa', 'AN'] @entity1277 :: ('MESH:D001068', 'Disease') :: ['eating disorders', 'eating disorder'] @entity1279 :: ('MESH:D052018', 'Disease') :: ['bulimia nervosa', 'BN'] @entity221 :: ('MESH:D005334', 'Disease') :: ['course of illness'] @entity5550 :: ('MESH:D056912', 'Disease') :: ['BED', 'binge eating disorder']
@entity1277 :: (MESH:D001068,Disease) :: ['eating disorders']
