Level of structural integration in people with schizophrenia and schizoaffective disorders - applicability and associations with clinical parameters.

Introduction: The psychic structure of people with psychosis has been the subject of theoretical and qualitative considerations. However, it has not been sufficiently studied quantitatively. Therefore, the aim of this study was to explore the structural abilities of people diagnosed with schizophrenia and schizoaffective psychosis using the Levels of Structural Integration Axis of the Operationalized Psychodynamic Diagnosis System (OPD-2-LSIA). The study aimed to determine possible associations between the OPD-2-LSIA and central parameters of illness. Additionally, possible structural differences between people diagnosed with schizophrenia and schizoaffective psychosis were tested. Methods: This cross-sectional study included 129 outpatients with schizophrenia or schizoaffective disorders. Measures of structural integration, symptom load, severity of illness, cognition, and social functioning were obtained. Descriptive statistics were used to analyze the overall structural level and the structural dimensions. Correlation coefficients were computed to measure the associations between OPD-2-LSIA and variables regarding the severity of illness and psychosocial functioning. Regression models were used to measure the influence of illness-related variables on OPD-2-LSIA, and the influence of OPD-2-LSIA on psychosocial functioning. Participants diagnosed with schizophrenia and schizoaffective disorders were examined with regard to possible group differences. Results: The results of the OPD-2-LSIA showed that the overall structural level was between 'moderate to low' and 'low level of structural integration'. Significant correlations were found between OPD-2-LSIA and psychotic symptoms (but not depressive symptoms), as well as between OPD-2-LSIA and psychosocial functioning. It was found that variables related to severity of illness had a significant impact on OPD-2-LSIA, with psychotic, but not depressive symptoms being significant predictors. OPD-2-LSIA was found to predict psychosocial functioning beyond symptoms and cognition. No significant differences were found between participants with schizophrenia and schizoaffective psychosis. There was also no correlation found between OPD-2-LSIA and depressive symptomatology (except for the subdimension Internal communication). Discussion: Contrary to theoretical assumptions, the results of the study show a heterogenous picture of the psychic structure of people with psychosis. The associations between OPD-2-LSIA and severity of illness, particularly psychotic symptomatology, as well as the influence of OPD-2-LSIA on psychosocial functioning, are discussed.

Exploring the Latent Structure and Convergent and Incremental Validity of the Metacognition Assessment Scale - Abbreviated in a Sample of Patients with Non-Affective Psychosis.

Synthetic metacognition is a heterogeneous construct related to psychotic disorders. One important tool to assess this construct is the Metacognition Assessment Scale - Abbreviated (MAS-A). In this study, we investigated the latent structure as well as the interrater reliability and convergent and incremental validity of the MAS-A in a sample of patients with non-affective psychosis. Analyses indicated that the scale might be one-dimensional. Interrater reliability of the MAS-A total score was good. In terms of convergent validity, correlational analyses showed significant associations of MAS-A metacognition with the Operationalized Psychodynamic Diagnosis Level of Structural Integration Axis (OPD-LSIA) and the Levels of Emotional Awareness Scale (LEAS). In terms of construct validity, a significant association was observed between MAS-A metacognition and a short version of the International Classification of Functioning, Disability and Health (MINI-ICF), which persisted after self-report measures of impairments in structural capacities (Structure Questionnaire of Operationalized Psychodynamic Diagnosis [OPD-SQS]) and mentalizing abilities (Mentalization Questionnaire [MZQ]) were included as covariates, but not after symptom dimensions were included. There was a significant correlation with the current living situation, but not with other external criteria like diagnosis or duration of illness. Future studies should explore alternative outcomes and replicate results in longitudinal designs.

Measuring Emotional Awareness in Patients With Schizophrenia and Schizoaffective Disorders.

The ability to mentalize (i.e., to form representations of mental states and processes of oneself and others) is often impaired in people with schizophrenia spectrum disorders. Emotional awareness (EA) represents one aspect of affective mentalizing and can be assessed with the Levels of Emotional Awareness Scale (LEAS), but findings regarding individuals with schizophrenia spectrum disorders are inconsistent. The present study aimed at examining the usability and convergent validity of the LEAS in a sample of N = 130 stabilized outpatients with schizophrenia or schizoaffective disorders. An adequacy rating was added to the conventional LEAS rating to account for distortions of content due to, for example, delusional thinking. Scores of the patient group were compared with those of a matched healthy control sample. Correlation with symptom clusters, a self-report measure of EA, a measure of synthetic metacognition (MAS-A-G), and an expert rating capturing EA from the psychodynamic perspective of psychic structure (OPD-LSIA) were examined. Regarding self-related emotional awareness, patients did not score lower than controls neither in terms of conventional LEAS nor in terms of adequacy. Regarding other-related emotional awareness, however, patients showed a reduced level of adequacy compared to controls whereas no such difference was found for conventional LEAS scores. Higher conventional LEAS scores were associated with fewer negative symptoms, and higher structural integration of self-perceptions measured by the OPD-LSIA. Higher adequacy of responses correlated with fewer symptoms of disorganization as well as excitement, higher scores of self-reflection on the MAS-A-G as well as self- and object-perception and internal and external communication as measured by the subscales of the OPD-LSIA. Findings suggest that the LEAS might not be sensitive enough to detect differences between mildly symptomatic patients with schizophrenia or schizoaffective disorders and healthy controls. However, LEAS ratings are still suitable to track intraindividual changes in EA over time. Observing the adequacy of patients' responses when using the LEAS may be a promising way to increase diagnostical utility and to identify patterns of formal and content-related alterations of mentalizing in this patient group. Methodological indications for future studies are discussed.

ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance.

INTRODUCTION: The US FDA is interested in a tool that would enable pharmacovigilance safety evaluators to automate the identification of adverse drug events (ADEs) mentioned in FDA prescribing information. The MITRE Corporation (MITRE) and the FDA organized a shared task-Adverse Drug Event Evaluation (ADE Eval)-to determine whether the performance of algorithms currently used for natural language processing (NLP) might be good enough for real-world use. OBJECTIVE: ADE Eval was conducted to evaluate a range of NLP techniques for identifying ADEs mentioned in publicly available FDA-approved drug labels (package inserts). It was designed specifically to reflect pharmacovigilance practices within the FDA and model possible pharmacovigilance use cases. METHODS: Pharmacovigilance-specific annotation guidelines and annotated corpora were created. Two metrics modeled the experiences of FDA safety evaluators: one measured the ability of an algorithm to identify correct Medical Dictionary for Regulatory Activities (MedDRA®) terms for the text from the annotated corpora, and the other assessed the quality of evidence extracted from the corpora to support the selected MedDRA® term by measuring the portion of annotated text an algorithm correctly identified. A third metric assessed the cost of correcting system output for subsequent training (averaged, weighted F1-measure for mention finding). RESULTS: In total, 13 teams submitted 23 runs: the top MedDRA® coding F1-measure was 0.79, the top quality score was 0.96, and the top mention-finding F1-measure was 0.89. CONCLUSION: While NLP techniques do not perform at levels that would allow them to be used without intervention, it is now worthwhile exploring making NLP outputs available in human pharmacovigilance workflows.

Levels of Structural Integration Mediate the Impact of Metacognition on Functioning in Non-affective Psychosis: Adding a Psychodynamic Perspective to the Metacognitive Approach.

Synthetic metacognition is defined by integrative and contextualizing processes of discrete reflexive moments. These processes are supposed to be needed to meet intrapsychic as well as interpersonal challenges and to meaningfully include psychotic experience in a personal life narrative. A substantial body of evidence has linked this phenomenon to psychosocial functioning and treatment options were developed. The concept of synthetic metacognition, measured with the Metacognition Assessment Scale-Abbreviated (MAS-A), rises hope to bridge gaps between therapeutic orientations and shares valuable parallels to modern psychodynamic constructs, especially the 'levels of structural integration' of the Operationalized Psychodynamic Diagnosis (OPD-2). As theoretical distinctions remain, aim of this study was to compare the predictive value of both constructs with regard to psychosocial functioning of patients with non-affective psychoses, measured with the International Classification of Functioning, Disability and Health (MINI-ICF-APP). It was further explored if levels of structural integration (OPD-LSIA) would mediate the impact of metacognition (MAS-A) on function (MINI-ICF-APP). Expert ratings of synthetic metacognition (MAS-A), the OPD-2 'levels of structural integration' axis (OPD-LSIA), psychosocial functioning (MINI-ICF-APP) and assessments of general cognition and symptoms were applied to 100 individuals with non-affective psychoses. Whereas both, MAS-A and OPD-LSIA, significantly predicted MINI-ICF-APP beyond cognition and symptoms, OPD-LSIA explained a higher share of variance and mediated the impact of MAS-A on MINI-ICF-APP. Levels of structural integration, including the quality of internalized object representations and unconscious interpersonal schemas, might therefore be considered as valuable predictors of social functioning and as one therapeutic focus in patients with non-affective psychoses. Structural integration might go beyond and form the base of a person's actual reflexive and metacognitive capabilities. Psychotherapeutic procedures specific for psychoses may promote and challenge a patient's metacognitive capacities, but should equally take the need for maturing structural skills into account. Modern psychodynamic approaches to psychosis are shortly presented, providing concepts and techniques for the implicit regulation of interpersonal experience and aiming at structural integration in this patient group.

Between a rock and a hard place: Associations between Mentzos' "dilemma", self-reported interpersonal problems, and psychosocial functioning in individuals with non-affective psychoses.

Primary aim of this study was to determine the extent and type of self-reported interpersonal problems in patients with non-affective psychoses and their impact on psychosocial functioning. Furthermore, we aimed to explore potential links with the psychodynamic construct of Stavros Mentzos' "psychotic dilemma", which describes an insufferable inner tension caused by an individual's struggle of being torn between "self-oriented" and "object-oriented" tendencies. In a cross-sectional study among 129 patients with non-affective psychoses, measures of cognition, symptom load and social functioning as well as a tentative, psychodynamic assessment of Mentzos' "dilemma" were obtained during a clinical research visit. Self-report data on interpersonal problems were gathered using the Inventory of Interpersonal Problems (IIP-64D) and compared with a German representative standard sample. Second, IIP-64D scores were compared between groups with or without Mentzos' "dilemma". Hierarchical regression analyses were performed to test for the impact of interpersonal problems on psychosocial functioning, while controlling for cognitive deficits and psychopathology. Results showed that IIP-64D scores differed significantly from healthy controls, except for "self-centred" and "intrusive" interpersonal styles. Participants with a potential "psychotic dilemma" scored significantly higher on the subscales: "domineering", "self-centred", "cold", and "socially avoidant" than the group without a "psychotic dilemma". The total amount of interpersonal problems, and particularly high scores on the IIP-64D "socially avoidant" subscale, predicted psychosocial dysfunction, whereas a "cold" interpersonal style had an opposite effect. In conclusion, specific interpersonal problems may predict psychotherapeutic outcome measures like psychosocial functioning and are partly compatible with the psychodynamic construct of Stavros Mentzos' "psychotic dilemma".

An annotation and modeling schema for prescription regimens.

BACKGROUND: We introduce TranScriptML, a semantic representation schema for prescription regimens allowing various properties of prescriptions (e.g. dose, frequency, route) to be specified separately and applied (manually or automatically) as annotations to patient instructions. In this paper, we describe the annotation schema, the curation of a corpus of prescription instructions through a manual annotation effort, and initial experiments in modeling and automated generation of TranScriptML representations. RESULTS: TranScriptML was developed in the process of curating a corpus of 2914 ambulatory prescriptions written within the Partners Healthcare network, and its schema is informed by the content of that corpus. We developed the representation schema as a novel set of semantic tags for prescription concept categories (e.g. frequency); each tag label is defined with an accompanying attribute framework in which the meaning of tagged concepts can be specified in a normalized fashion. We annotated a subset (1746) of this dataset using cross-validation and reconciliation between multiple annotators, and used Conditional Random Field machine learning and various other methods to train automated annotation models based on the manual annotations. The TranScriptML schema implementation, manual annotation, and machine learning were all performed using the MITRE Annotation Toolkit (MAT). We report that our annotation schema can be applied with varying levels of pairwise agreement, ranging from low agreement levels (0.125 F for the relatively rare REFILL tag) to high agreement levels approaching 0.9 F for some of the more frequent tags. We report similarly variable scores for modeling tag labels and spans, averaging 0.748 F-measure with balanced precision and recall. The best of our various attribute modeling methods captured most attributes with accuracy above 0.9. CONCLUSIONS: We have described an annotation schema for prescription regimens, and shown that it is possible to annotate prescription regimens at high accuracy for many tag types. We have further shown that many of these tags and attributes can be modeled at high accuracy with various techniques. By structuring the textual representation through annotation enriched with normalized values, the text can be compared against the pharmacist-entered structured data, offering an opportunity to detect and correct discrepancies.

Microwave Characterization of Ba-Substituted PZT and ZnO Thin Films.

The microwave dielectric properties of (Ba0.1Pb0.9)(Zr0.52Ti0.48)O3 (BPZT) and ZnO thin films with thicknesses below were investigated. No significant dielectric relaxation was observed for both BPZT and ZnO up to 30 GHz. The intrinsic dielectric constant of BPZT was as high as 980 at 30 GHz. The absence of strong dielectric dispersion and loss peaks in the studied frequency range can be linked to the small grain diameters in these ultrathin films.

The Metacognition Assessment Scale (MAS-A): Results of a pilot study applying a German translation to individuals with schizophrenia spectrum disorders.

OBJECTIVES: Metacognition, the capacity 'to think about thinking' and thus to reflect and to master interpersonal problems on a mentalistic basis, is often impaired among patients with schizophrenia spectrum disorders and has been suggested as a potential treatment target. However, little is known about the reliability of its measurement and links with related phenomena. The aim of this study was to validate a German translation of the Metacognition Assessment Scale (MAS-A) as a measure to assess metacognition from free narratives of patients' personally relevant episodes and relationships. DESIGN AND METHODS: MAS-A was applied to narratives of 22 individuals with schizophrenia spectrum disorders together with self-ratings and behavioural tests of metacognitive and related functions such as mentalizing and emotional awareness. Multi-level modelling allowed to calculate inter-rater reliability (IRR) and inter-rater agreement (IRA) and to include test results as level-2 predictors of the aggregated scorings on the MAS-A subscales in order to explore convergent validity. After considering neurocognition and symptom scores as further predictors, aggregated scorings were correlated with psychosocial functioning. RESULTS: There were high IRRs and IRAs all over the ratings. None of the related measures accounted for variance in MAS-A scorings, indicating the existence of separable, non-overlapping constructs. Verbal memory and positive symptoms were significant predictors for MAS-A subscales. MAS-A, but no other measure, displayed significant associations with psychosocial functioning. CONCLUSIONS: MAS-A is a reliable expert rating to assess metacognition from patients' free narratives. Considering the link to psychosocial functioning, MAS-A appears to be a promising tool for the evaluation of metacognition. PRACTITIONER POINTS: MAS-A is a reliable tool to evaluate metacognitive function from narratives about emotionally relevant topics and meaningful relationships. Metacognition appears separate from neighbouring constructs such as mentalizing, ToM, or emotional awareness. MAS-A scales are significantly predicted by verbal memory and positive symptoms. Only MAS-A scales display significant associations with psychosocial functioning, and it thus is a promising tool to evaluate metacognition in psychotherapy research.

Ease of adoption of clinical natural language processing software: An evaluation of five systems.

OBJECTIVE: In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions. MATERIALS AND METHODS: A team of human evaluators performed a series of scripted adoptability test tasks with each of the participating systems. The evaluation team consisted of four "expert evaluators" with training in computer science, and eight "end user evaluators" with mixed backgrounds in medicine, nursing, pharmacy, and health informatics. We assessed how easy it is to adopt the submitted systems along the following three dimensions: communication effectiveness (i.e., how effective a system is in communicating its designed objectives to intended audience), effort required to install, and effort required to use. We used a formal software usability testing tool, TURF, to record the evaluators' interactions with the systems and 'think-aloud' data revealing their thought processes when installing and using the systems and when resolving unexpected issues. RESULTS: Overall, the ease of adoption ratings that the five systems received are unsatisfactory. Installation of some of the systems proved to be rather difficult, and some systems failed to adequately communicate their designed objectives to intended adopters. Further, the average ratings provided by the end user evaluators on ease of use and ease of interpreting output are -0.35 and -0.53, respectively, indicating that this group of users generally deemed the systems extremely difficult to work with. While the ratings provided by the expert evaluators are higher, 0.6 and 0.45, respectively, these ratings are still low indicating that they also experienced considerable struggles. DISCUSSION: The results of the Track 3 evaluation show that the adoptability of the five participating clinical NLP systems has a great margin for improvement. Remedy strategies suggested by the evaluators included (1) more detailed and operation system specific use instructions; (2) provision of more pertinent onscreen feedback for easier diagnosis of problems; (3) including screen walk-throughs in use instructions so users know what to expect and what might have gone wrong; (4) avoiding jargon and acronyms in materials intended for end users; and (5) packaging prerequisites required within software distributions so that prospective adopters of the software do not have to obtain each of the third-party components on their own.

Bootstrapping a de-identification system for narrative patient records: cost-performance tradeoffs.

PURPOSE: We describe an experiment to build a de-identification system for clinical records using the open source MITRE Identification Scrubber Toolkit (MIST). We quantify the human annotation effort needed to produce a system that de-identifies at high accuracy. METHODS: Using two types of clinical records (history and physical notes, and social work notes), we iteratively built statistical de-identification models by annotating 10 notes, training a model, applying the model to another 10 notes, correcting the model's output, and training from the resulting larger set of annotated notes. This was repeated for 20 rounds of 10 notes each, and then an additional 6 rounds of 20 notes each, and a final round of 40 notes. At each stage, we measured precision, recall, and F-score, and compared these to the amount of annotation time needed to complete the round. RESULTS: After the initial 10-note round (33min of annotation time) we achieved an F-score of 0.89. After just over 8h of annotation time (round 21) we achieved an F-score of 0.95. Number of annotation actions needed, as well as time needed, decreased in later rounds as model performance improved. Accuracy on history and physical notes exceeded that of social work notes, suggesting that the wider variety and contexts for protected health information (PHI) in social work notes is more difficult to model. CONCLUSIONS: It is possible, with modest effort, to build a functioning de-identification system de novo using the MIST framework. The resulting system achieved performance comparable to other high-performing de-identification systems.

Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text.

OBJECTIVE: Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS). MATERIALS AND METHODS: HIPS relies on obfuscation to conceal residual identifiers. According to this theory, replacing the detected identifiers with realistic but synthetic surrogates should collectively render the few 'leaked' identifiers difficult to distinguish from the synthetic surrogates. The authors conducted a pilot study to test this theory on clinical narrative, de-identified by an automated system. Test corpora included 31 oncology and 50 family practice progress notes read by two trained chart abstractors and an informaticist. RESULTS: Experimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information. DISCUSSION: This pilot test suggests HIPS is feasible, but requires further evaluation. The results need to be replicated on larger corpora of diverse origin under a range of detection scenarios. Error analyses also suggest areas where surrogate generation techniques can be refined to improve efficacy. CONCLUSIONS: If these results generalize to existing high-performing de-identification systems with recall rates of 94-98%, HIPS could increase the effective de-identification rates of these systems to levels above 99% without further advancements in system recall. Additional and more rigorous assessment of the HIPS approach is warranted.

The MITRE Identification Scrubber Toolkit: design, training, and assessment.

PURPOSE: Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information. METHODS: MIST was evaluated with four classes of patient records from the Vanderbilt University Medical Center: discharge summaries, laboratory reports, letters, and order summaries. We trained and tested MIST on each class of record separately, as well as on pooled sets of records. We measured precision, recall, F-measure and accuracy at the word level for the detection of patient identifiers as designated by the HIPAA Safe Harbor Rule. RESULTS: MIST was applied to medical records that differed in the amounts and types of protected health information (PHI): lab reports contained only two types of PHI (dates, names) compared to discharge summaries, which were much richer. Performance of the de-identification tool depended on record class; F-measure results were 0.996 for order summaries, 0.996 for discharge summaries, 0.943 for letters and 0.934 for laboratory reports. Experiments suggest the tool requires several hundred training exemplars to reach an F-measure of at least 0.9. CONCLUSIONS: The MIST toolkit makes possible the rapid tailoring of automated de-identification to particular document types and supports the transition of the de-identification software to medical end users, avoiding the need for developers to have access to original medical records. We are making the MIST toolkit available under an open source license to encourage its application to diverse data sets at multiple institutions.

Effects of personal identifier resynthesis on clinical text de-identification.

OBJECTIVE: De-identified medical records are critical to biomedical research. Text de-identification software exists, including "resynthesis" components that replace real identifiers with synthetic identifiers. The goal of this research is to evaluate the effectiveness and examine possible bias introduced by resynthesis on de-identification software. DESIGN: We evaluated the open-source MITRE Identification Scrubber Toolkit, which includes a resynthesis capability, with clinical text from Vanderbilt University Medical Center patient records. We investigated four record classes from over 500 patients' files, including laboratory reports, medication orders, discharge summaries and clinical notes. We trained and tested the de-identification tool on real and resynthesized records. MEASUREMENTS: We measured performance in terms of precision, recall, F-measure and accuracy for the detection of protected health identifiers as designated by the HIPAA Safe Harbor Rule. RESULTS: The de-identification tool was trained and tested on a collection of real and resynthesized Vanderbilt records. Results for training and testing on the real records were 0.990 accuracy and 0.960 F-measure. The results improved when trained and tested on resynthesized records with 0.998 accuracy and 0.980 F-measure but deteriorated moderately when trained on real records and tested on resynthesized records with 0.989 accuracy 0.862 F-measure. Moreover, the results declined significantly when trained on resynthesized records and tested on real records with 0.942 accuracy and 0.728 F-measure. CONCLUSION: The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records). The resynthesis component regularizes the data to make them less "realistic," resulting in loss of performance particularly when training on resynthesized data and testing on real data.