Das Biofeedback des Körperschwankens stellt eine Möglichkeit dar, Patienten mit statischen und dynamischen Gleichgewichtsproblemen alternative Sinnesreize zur Erlangung eines stabilen Gleichgewichts zur Verfügung zu stellen, was zu einer Verbesserung ihrer Lebensqualität führt. Das Biofeedback-System erzeugt mittels am Kopf befestigter Signalwandler auditive, vibro-taktile und vibro-vestibuläre Sinneseindrücke. Die Ergebnisse sowohl an jungen als auch an älteren Probanden ergaben, dass mit Hilfe des Biofeedbacks eine bemerkenswerte 40-60%ige Reduktion des Körperschwankens erzielt werden konnte.Biofeedback of body sway is one means of providing an alternative sensory input to reach a stable balance for patients with static and dynamic balance impairments which can lead to an improvement of their quality of life. The Biofeedback system provides auditory, vibrotactile and vibro-vestibular sensory inputs via transducers that are placed around the head. The results on both young and elderly subjects indicate that noteworthy 40-60% reductions in body sway could be achieved.
Objectives
Identification of diagnostic error is complex and mostly relies on expert ratings, a severely limited procedure. We developed a system that allows to automatically identify diagnostic labelling error from diagnoses coded according to the international classification of diseases (ICD), often available as routine health care data.
Methods
The system developed (index test) was validated against rater based classifications taken from three previous studies of diagnostic labeling error (reference standard). The system compares pairs of diagnoses through calculation of their distance within the ICD taxonomy. Calculation is based on four different algorithms. To assess the concordance between index test and reference standard, we calculated the area under the receiver operating characteristics curve (AUROC) and corresponding confidence intervals. Analysis were conducted overall and separately per algorithm and type of available dataset.
Results
Diagnoses of 1,127 cases were analyzed. Raters previously classified 24.58% of cases as diagnostic labelling errors (ranging from 12.3 to 87.2% in the three datasets). AUROC ranged between 0.821 and 0.837 overall, depending on the algorithm used to calculate the index test (95% CIs ranging from 0.8 to 0.86). Analyzed per type of dataset separately, the highest AUROC was 0.924 (95% CI 0.887–0.962).
Conclusions
The trigger system to automatically identify diagnostic labeling error from routine health care data performs excellent, and is unaffected by the reference standards’ limitations. It is however only applicable to cases with pairs of diagnoses, of which one must be more accurate or otherwise superior than the other, reflecting a prevalent definition of a diagnostic labeling error.
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