Elemental fingerprint: Reassessment of a cerebrospinal fluid biomarker for Parkinson's disease.

The aim of the study was to validate a predictive biomarker machine learning model for the classification of Parkinson's disease (PD) and age-matched controls (AMC), based on bioelement abundance in the cerebrospinal fluid (CSF). For this multicentric trial, participants were enrolled from four different centers. CSF was collected according to standardized protocols. For bioelement determination, CSF samples were subjected to inductively coupled plasma mass spectrometry. A predefined Support Vector Machine (SVM) model, trained on a previous discovery cohort was applied for differentiation, based on the levels of six different bioelements. 82 PD patients, 68 age-matched controls and 7 additional Normal Pressure Hydrocephalus (NPH) patients were included to validate a predefined SVM model. Six differentiating elements (As, Fe, Mg, Ni, Se, Sr) were quantified. Based on their levels, SVM was successfully applied to a new local cohort (AUROC 0.76, Sensitivity 0.80, Specificity 0.83), without taking any additional features into account. The same model did not discriminate PD and AMCs / NPH from three external cohorts, likely due to center effects. However, discrimination was possible in cohorts with a full elemental data set, now using center-specific discovery cohorts and a cross validated approach (AUROC 0.78 and 0.88, respectively). Pooled PD CSF iron levels showed a clear correlation with disease duration (p = .0001). In summary, bioelemental CSF patterns, obtained by mass spectrometry and integrated into a predictive model yield the potential to facilitate the differentiation of PD and AMC. Center-specific biases interfere with application in external cohorts. This must be carefully addressed using center-defined, local reference values and models.

Extension, Compression, and Beyond: A Unique Classification System for Mortality Evolution Patterns.

A variety of literature addresses the question of how the age distribution of deaths changes over time as life expectancy increases. However, corresponding terms such as extension, compression, or rectangularization are sometimes defined only vaguely, and statistics used to detect certain scenarios can be misleading. The matter is further complicated because mixed scenarios can prevail, and the considered age range can have an impact on observed mortality patterns. In this article, we establish a unique classification framework for realized mortality scenarios that allows for the detection of both pure and mixed scenarios. Our framework determines whether changes of the deaths curve over time show elements of extension or contraction; compression or decompression; left- or right-shifting mortality; and concentration or diffusion. The framework not only can test the presence of a particular scenario but also can assign a unique scenario to any observed mortality evolution. Furthermore, it can detect different mortality scenarios for different age ranges in the same population. We also present a methodology for the implementation of our classification framework and apply it to mortality data for U.S. females.

DEGAM criteria catalogue for training practices in primary care--a proposal for the assessment of the structural quality of training practices.

BACKGROUND: Whilst the structure of primary care vocational training in Germany is being increasingly formalized there remains an abundance of disparate locally defined criteria for the training practices. Advanced medical training in the ambulatory setting has also been identified as an area of need by other specialties. GOAL: In contrast to the current practice of a unregulated authorization by regional medical associations this catalogue provide transparent, clearly defined criteria for the assignment of training practice status. METHODS: The first draft of the criteria catalogue integrates feedback from 30 academic general practitioners. The feasibility of the catalogue was tested by a further 30 surgeries. Analysis included an assessment of the sociodemographic characteristics of the trainers and their practices as well as satisfaction of the participants with the approved authorization period. RESULTS: The criteria catalogue comprises 19 items within the domains of trainer qualification, practice infrastructure and patient specific factors as well as mandatory criteria. The points scored through this system confer a variable period of authorization. Of the 30 participants 17 were satisfied with the period of authorization they received, 10 were dissatisfied, and one was indifferent. Satisfaction showed no correlation with sex, experience as a trainer, or with the score achieved through the criteria catalogue. It correlated little with the length of time practicing as a doctor. CONCLUSION: The criteria catalogue reflects both the breadth of general practice as well as the skills of the trainers. Satisfaction of participants in the test group was good, and infers a basis for applying the catalogue through regional medical associations to assign teaching practice status. It may also be used as a blue-print for other medical specialties.