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A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.
Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.
Cognitive data significantly classified patients from controls (accuracies = 60–69%; p values = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.
In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.
The patient's experience of the clinician is an increasingly important
area in time of ‘consumer choice’ and appraisal of the individual
practitioner. Validated, easy-to-use scales are scarce. The aim was to
validate a user-friendly, brief scale measuring patient satisfaction
(PatSat scale). Over three phases, patients were involved in developing
and validating the scale against the Verona satisfaction subscale.
A highly significant correlation was found between the two scales
(Spearman's correlation coefficient 0.97, two-tailed P
The PatSat is a new patient satisfaction scale validated in a psychiatric
out-patient population. It appeared popular with patients and took less
than 1 minute to fill in. The use of validated scales measuring patient
satisfaction is a pivotal part of mental health delivery and advancing
overall quality of care.
To evaluate compliance with the national recommendation on supplemental iron to all pregnant women in Denmark and to explore differences between compliers and non-compliers with respect to dietary habits and other lifestyle factors.
Intake of supplemental iron from pure iron supplements and from multivitamin and mineral preparations was estimated in mid-pregnancy.
Nationwide cohort study, the Danish National Birth Cohort (DNBC), comprising more than 100 000 women recruited in early pregnancy.
Information on diet and dietary supplements was available for 54 371 women. Of these, information on lifestyle factors was available for 50 902 women.
A high compliance with the recommendation was found, as approximately 77% of the women reported use of iron supplements during pregnancy. However, many of the compliers did not obtain the recommended doses of iron, which can partly be explained by the lack of iron preparations of appropriate doses available on the Danish market. Compliance with the recommendation was associated with age above 20 years, primiparity, body mass index < 30 kg m− 2, non-smoking and long education. No major differences were seen in dietary intake between compliers and non-compliers.
Overall, a high compliance rate was found among participants of the DNBC but a clarification on daily dose is needed, and more concern should be paid to vulnerable groups such as young, smoking women and women with no or short education.
The undergraduate medical education in Denmark consists of six-and-a-half years of (mainly) academic studying. A combination of sabbatical years and late-starters pushed the average age of the medical candidate to 30 in 1998. Well over 50% are women.
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