Relationship between Neurosis and Personality Disorder

1983 ◽  
Vol 142 (4) ◽  
pp. 404-408 ◽  
Author(s):  
Peter Tyrer ◽  
Patricia Casey ◽  
Joanna Gall
Keyword(s):  
Clinical Trial ◽  
Personality Disorder ◽  
Clinical Outcome ◽  
Psychiatric Patients ◽  
Anxiety Neurosis ◽  
Interview Schedule ◽  
International Classification Of Disease ◽  
Neurotic Disorders ◽  
Obsessional Neurosis

Summary316 psychiatric patients—predominantly out-patients—with a clinical diagnosis of neurosis (International Classification of Disease), were given an interview schedule for assessing personality disorders. Nearly two fifths were rated as having personality disorder, the anankastic type being the most common and sociopathic personality disorder the least. Patients with anxiety, phobic and obsessional neurosis were all significantly more likely to show personality disorder than those with depressive neurosis. Broadly, passive-dependent personalities were linked to anxiety neurosis, anankastic personality disorder to obsessional neurosis, and both anankastic and passive-dependent personalities to phobic neurosis. In a clinical trial of the efficacy of two dosage regimes of phenelzine in 60 patients the presence of personality disorder significantly impaired clinical outcome. The results suggest strongly that the assessment of personality in neurotic disorders is necessary to make an adequate diagnosis and predict the outcome of treatment.

Classification of Personality Disorder

1979 ◽  
Vol 135 (2) ◽  
pp. 163-167 ◽  
Author(s):  
Peter Tyrer ◽  
John Alexander
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Personality Disorder ◽  
Personality Disorders ◽  
Psychiatric Patients ◽  
Interview Schedule ◽  
Personality Variables ◽  
Current Classification ◽  
Personality Disordered

SummaryAn interview schedule was used to record the personality traits of 130 psychiatric patients, 65 with a primary clinical diagnosis of personality disorder and 65 with other diagnoses. The results were analysed by factor analysis and three types of cluster analysis. Factor analysis showed a similar structure of personality variables in both groups of patients, supporting the notion that personality disorders differ only in degree from the personalities of other psychiatric patients. Cluster analysis revealed five discrete categories; sociopathic, passive-dependent, anankastic, schizoid and a non-personality-disordered group. Of all the personality-disordered patients 63 per cent fell into the passive-dependent or sociopathic category. The results suggest that the current classification of personality disorder could be simplified.

scholarly journals Relationship Pattern of Personality Disorder Traits in Major Psychiatric Disorders: A Cross-Sectional Study

2021 ◽  
pp. 025371762199953
Author(s):  
Bhavneesh Saini ◽  
Pir Dutt Bansal ◽  
Mamta Bahetra ◽  
Arvind Sharma ◽  
Priyanka Bansal ◽  
...  
Keyword(s):  
Personality Disorder ◽  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Age Of Onset ◽  
Psychiatric Patients ◽  
Cross Sectional Study ◽  
Psychotic Symptoms ◽  
Sectional Study ◽  
Cross Sectional ◽  
Neurotic Disorders

Background: Normal personality development, gone awry due to genetic or environmental factors, results in personality disorders (PD). These often coexist with other psychiatric disorders, affecting their outcome adversely. Considering the heterogeneity of data, more research is warranted. Methods: This was a cross-sectional study on personality traits in psychiatric patients of a tertiary hospital, over 1 year. Five hundred and twenty-five subjects, aged 18–45 years, with substance, psychotic, mood, or neurotic disorders were selected by convenience sampling. They were evaluated for illness-related variables using psychiatric pro forma; diagnostic confirmation and severity assessment were done using ICD-10 criteria and suitable scales. Personality assessment was done using the International Personality Disorder Examination after achieving remission. Results: Prevalence of PD traits and PDs was 56.3% and 4.2%, respectively. While mood disorders were the diagnostic group with the highest prevalence of PD traits, it was neurotic disorders for PDs. Patients with PD traits had a past psychiatric history and upper middle socioeconomic status (SES); patients with PDs were urban and unmarried. Both had a lower age of onset of psychiatric illness. Psychotic patients with PD traits had higher and lower PANSS positive and negative scores, respectively. The severity of personality pathology was highest for mixed cluster and among neurotic patients. Clusterwise prevalence was cluster C > B > mixed > A (47.1%, 25.2%, 16.7%, and 11.4%). Among subtypes, anankastic (18.1%) and mixed (16.7%) had the highest prevalence. Those in the cluster A group were the least educated and with lower SES than others. Conclusions: PD traits were present among 56.3% of the patients, and they had many significant sociodemographic and illness-related differences from those without PD traits. Cluster C had the highest prevalence. Among patients with psychotic disorders, those with PD traits had higher severity of psychotic symptoms.

Differences between Two Studies of Hand Preference in Psychiatric Patients

1982 ◽  
Vol 140 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Pamela J. Taylor ◽  
Rosemary Dalton ◽  
J. J. Fleminger ◽  
W. A. Lishman
Keyword(s):  
Personality Disorder ◽  
Sample Size ◽  
Psychiatric Patients ◽  
Hand Preference ◽  
Left Handedness ◽  
Simple Classification ◽  
Normal Controls

SummarySignificant differences in handedness patterns between groups of psychiatric patients and normal controls were identified in two recent British studies, with substantial disagreement in some important findings. Most of the discrepancies were attributable to the different application of a simple classification of handedness data, and the remainder to differences in sample size. Diagnosis, sex and age were then found to have a similar effect on handedness in both studies. Neurotic patients were similar to controls regardless of classification, whereas mixed handedness in personality disorder depended on it. There was no overall excess of left-handedness among schizophrenics, but trends towards excess sinistrality in men and full dextrality in women approached significance.

Temporal reliability of personality in psychiatric patients

1983 ◽  
Vol 13 (2) ◽  
pp. 393-398 ◽  
Author(s):  
P. Tyrer ◽  
J. Strauss ◽  
D. Cicchetti
Keyword(s):  
Personality Disorder ◽  
Personality Traits ◽  
Personality Assessment ◽  
Clinical Symptoms ◽  
Psychiatric Patients ◽  
Chronic Patients ◽  
Interview Schedule ◽  
Individual Personality ◽  
Categorical Diagnosis ◽  
Severe Personality Disorder

SYNOPSISThe Personality Assessment Schedule, an interview schedule specifically designed for assessing personality disorder, was administered twice to 28 psychiatric patients, with a mean interval of 2·9 years between each assessment. The first assessment was made by a psychiatrist and the second by a medical student who had no prior knowledge of the patients. The reliability of the 2 assessments was measured using 4 different techniques. Although the reliability of individual personality traits was inconsistent over time, the categorical diagnosis of personality disorder was good (Kωw = 0·64), giving some support to the validity of the schedule. Reasons for discordance in the assessments were examined and appeared to be due mainly to confusion between clinical symptoms and personality traits, retrospective errors in recording past personality in chronic patients, and special difficulties in determining the primary abnormality in severe personality disorder.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Future Directions

2020 ◽  
pp. 476-479
Author(s):  
Marc N. Potenza ◽  
Kyle A. Faust ◽  
David Faust
Keyword(s):  
Technology Development ◽  
Digital Technologies ◽  
World Health ◽  
International Classification Of Disease ◽  
Levels Of Use ◽  
Pornography Use ◽  
Negative Impacts ◽  
Mental Health Treatments ◽  
The Many

As digital technology development continues to expand, both its positive and negative applications have also grown. As such, it is essential to continue gathering data on the many types of digital technologies, their overall effects, and their impact on public health. The World Health Organization’s inclusion of Gaming Disorder in the eleventh edition of the International Classification of Disease (ICD-11) indicates that some of the problematic effects of gaming are similar to those of substance-use disorders and gambling. Certain behaviors easily engaged in via the internet may also lead to compulsive levels of use in certain users, such as shopping or pornography use. In contrast, digital technologies can also lead to improvements in and wider accessibility to mental health treatments. Furthermore, various types of digital technologies can also lead to benefits such as increased productivity or social functioning. By more effectively understanding the impacts of all types of digital technologies, we can aim to maximize their benefits while minimizing or preventing their negative impacts.

scholarly journals Intraoperative protective ventilation in patients undergoing major neurosurgical interventions: a randomized clinical trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Federico Longhini ◽  
Laura Pasin ◽  
Claudia Montagnini ◽  
Petra Konrad ◽  
Andrea Bruni ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Outcome ◽  
Adverse Events ◽  
Respiratory Rate ◽  
Randomized Clinical Trial ◽  
Protective Ventilation ◽  
Adult Patients ◽  
Control Group ◽  
Neurosurgical Procedures ◽  
Neurosurgical Patients

Abstract Background Post-operative pulmonary complications (PPC) can develop in up to 13% of patients undergoing neurosurgical procedures and may adversely affect clinical outcome. The use of intraoperative lung protective ventilation (LPV) strategies, usually including the use of a low Vt, low PEEP and low plateau pressure, seem to reduce the risk of PPC and are strongly recommended in almost all surgical procedures. Nonetheless, feasibility of LPV strategies in neurosurgical patients are still debated because the use of low Vt during LPV might result in hypercapnia with detrimental effects on cerebrovascular physiology. Aim of our study was to determine whether LPV strategies would be feasible compared with a control group in adult patients undergoing cranial or spinal surgery. Methods This single-centre, pilot randomized clinical trial was conducted at the University Hospital “Maggiore della Carità” (Novara, Italy). Adult patients undergoing major cerebral or spinal neurosurgical interventions with risk index for pulmonary post-operative complications > 2 and not expected to need post-operative intensive care unit (ICU) admission were considered eligible. Patients were randomly assigned to either LPV (Vt = 6 ml/kg of ideal body weight (IBW), respiratory rate initially set at 16 breaths/min, PEEP at 5 cmH2O and application of a recruitment manoeuvre (RM) immediately after intubation and at every disconnection from the ventilator) or control treatment (Vt = 10 ml/kg of IBW, respiratory rate initially set at 6–8 breaths/min, no PEEP and no RM). Primary outcomes of the study were intraoperative adverse events, the level of cerebral tension at dura opening and the intraoperative control of PaCO2. Secondary outcomes were the rate of pulmonary and extrapulmonary complications, the number of unplanned ICU admissions, ICU and hospital lengths of stay and mortality. Results A total of 60 patients, 30 for each group, were randomized. During brain surgery, the number of episodes of intraoperative hypercapnia and grade of cerebral tension were similar between patients randomized to receive control or LPV strategies. No difference in the rate of intraoperative adverse events was found between groups. The rate of postoperative pulmonary and extrapulmonary complications and major clinical outcomes were similar between groups. Conclusions LPV strategies in patients undergoing major neurosurgical intervention are feasible. Larger clinical trials are needed to assess their role in postoperative clinical outcome improvements. Trial registration registered on the Australian New Zealand Clinical Trial Registry (www.anzctr.org.au), registration number ACTRN12615000707561.

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