Background

Traumatic brain injury (TBI) is one of the most important pediatric conditions worldwide. In Germany, hospitalization rates for mild TBI drastically exceed hospitalization rates from similar healthcare systems.

Methods

The SaVeBRAIN.Kids trial will implement and test a novel care pathway (nCP) for evidence-based standardized risk assessment, structured observation in the emergency department (ED) for several hours, and technology-supported home monitoring with the aim to reduce hospitalizations. This non-inferiority multicenter study will be carried out using a cluster-randomized stepped-wedge design, with all centers starting in the control phase and sequentially transitioning to the intervention. Eligible participants (age ≥ 3 months and < 18 years) must present within 48 h of head injury, have minimal symptoms (Glasgow coma scale ≥ 14), and no risk factors for intracranial complications. The co-primary outcomes are the relative risk of hospitalization and the proportion of unplanned re-visits within 72 h of presentation to the ED for ambulatory cases. Secondary outcomes include clinical safety measures, cost-effectiveness, and process evaluation. Based on power calculations (α = 0.05, power = 0.9), 1390 patients will be recruited over 12 months.

Discussion

 The SaVeBRAIN.Kids trial addresses a relevant healthcare challenge by testing a new approach to pediatric mild TBI management in Germany. It aligns with current evidence while accounting for the country’s specific healthcare context. If successful, the intervention could substantially reduce unnecessary hospitalizations and free inpatient capacities while preserving patient safety.

Trial registration

German Clinical Trials Registry (DRKS00035623). Registered on January 21, 2025.

Background

The impact of the infecting SARS-CoV-2 variant of concern (VOC) and the vaccination status was determined on the magnitude, breadth, and durability of the neutralizing antibody (nAb) profile in a longitudinal multicentre cohort study.

Methods

173 vaccinated and 56 non-vaccinated individuals were enrolled after SARS-CoV-2 Alpha, Delta, or Omicron infection and visited four times within 6 months and nAbs were measured for D614G, Alpha, Delta, BA.1, BA.2, BA.5, BQ.1.1, XBB.1.5 and JN.1.

Findings

Magnitude-breadth-analysis showed enhanced neutralization capacity in vaccinated individuals against multiple VOCs. Longitudinal analysis revealed sustained neutralization magnitude-breadth after antigenically distant Delta or Omicron breakthrough infection (BTI), with triple-vaccinated individuals showing significantly elevated titres and improved breadth. Antigenic mapping and antibody landscaping revealed initial boosting of vaccine-induced WT-specific responses after BTI, a shift in neutralization towards infecting VOCs at peak responses and an immune imprinted bias towards dominating WT immunity in the long-term. Despite that bias, machine-learning models confirmed a sustained shift of the immune-profiles following BTI.

Interpretation

In summary, our longitudinal analysis revealed delayed and short lived nAb shifts towards the infecting VOC, but an immune imprinted bias towards long-term vaccine induced immunity after BTI.

Funding

This work was funded by the Bavarian State Ministry of Science and the Arts for the CoVaKo study and the ForCovid project. The funders had no influence on the study design, data analysis or data interpretation.

This citizen science project CLUE compared the attack frequency between school and vacation periods among adolescents. The data collection process adopted in citizen science projects opens up the possibility of conducting analyses by including a large number of participants over a long period and across different regions. The data on 684 migraine attacks reported by 68 adolescents aged 16 to 19 years were collected using an online platform and smartphone apps. A Fisher’s exact test was used to compare the distributions of the migraine attack frequency during vacation and school periods in two different scenarios. In both scenarios, the attack frequency during school periods was significantly higher than that during vacation periods. The use of web-based data collection has some methodological limitations; however, it enabled the measurement of relative migraine attack frequency in students during vacation and school periods. The higher prevalence of migraine during school periods indicates the requirement of increasing headache awareness among children.

Objectives The purpose of this work is the analysis of migraine attack reports collected online within the project Migraine Radar in respect to the distribution of the migraine attacks over the week on a single-participant level. Materials & Methods Recording data using a web app as well as smartphone apps made it possible to collect data of 44 639 migraine attacks of 1085 participants who reported seven or more attacks over a participation period of at least 90 days. This allows the investigation of attack distributions on a single-participant level. Considering the day of the week with the highest attack frequency for each participant—the mode of the individual distribution—allows identifying participants suffering from weekend migraines. Namely, a weekend pattern is assumed if the mode falls on a Saturday or Sunday. Results For 15.9% of the participants, the attacks were not distributed equally (P < 0.05) over the days of the week. Instead, participants show different individual patterns for the distribution of their migraine attacks. Furthermore, the modes of the individual distributions are not distributed equally over the week. In fact, Saturday seems to be the predominant day for migraine attacks for a greater proportion of participants (195 of 1085). Conclusions Concerning the individual attack distributions, we found that participants show individual attack patterns and weekend migraine can be determined for a subgroup of participants, while other participants show accumulations of their attacks on other days of the week.

In this paper, we describe a new algorithm designed to generate lexical resources in the field of sentiment analysis. For this approach, based on corpora of customer reviews, we determine words and phrases as candidates for our sentiment lexicon solely by calculating a word co-occurrence measure and by considering word frequencies. The sentiment values of every single word or phrase are derived automatically from the review titles and the associated given ratings. We consciously renounce the use of natural language processing methods in order to ensure language independency of our algorithm. Furthermore, by using exclusively statistical methods, we are able to identify rather unusual word combinations, such as idiomatic expressions. This differentiates our work from most prior approaches which concentrate on single words or word-modifier combinations. An example lexicon is generated by the use of a corpus of 1.5 million German Amazon customer reviews.

Mit der zunehmenden Menge textueller Daten im Web 2.0 wächst auch die Notwendigkeit der maschinellen Auswertung dieser Daten, beispielsweise um in Texten geäußerte Meinungen aufzuspüren (Opinion Mining). Im vorliegenden Beitrag wird das Aspect-based Opinion Mining – ein Verfahren mit sehr hohem Detaillierungsgrad – für deutschsprachige Texte anhand eines Projekts für die Versicherungswirtschaft vorgestellt. Es wird gezeigt, dass in Bewertungsplattformen geäußerte Meinungen zu Produkten und Services von Versicherungen mit einer Genauigkeit von etwa 90% und einer Vollständigkeit von ca. 80% für positive und ca. 60% für negative Meinungen erkannt werden können.

In this work, we present a system called PoliTwi, which was designed to detect emerging political topics (Top Topics) in Twitter sooner than other standard information channels. The recognized Top Topics are shared via different channels with the wider public. For the analysis, we have collected about 4,000,000 tweets before and during the parliamentary election 2013 in Germany, from April until September 2013. It is shown, that new topics appearing in Twitter can be detected right after their occurrence. Moreover, we have compared our results to Google Trends. We observed that the topics emerged earlier in Twitter than in Google Trends.

Finally, we show how these topics can be used to extend existing knowledge bases (web ontologies or semantic networks) which are required for concept-level sentiment analysis. For this, we utilized special Twitter hashtags, called sentiment hashtags, used by the German community during the parliamentary election.

In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach. We use a phrase-based opinion lexicon for the German language to investigate, how good strong positive and strong negative expressions of opinions, concerning products and services in the insurance domain, can be detected. We perform experiments on hand-tagged statements expressing opinions retrieved from the Ciao platform. The initial corpus contained about 14,000 sentences from 1,600 reviews. For both, positive and negative statements, more than 100 sentences were tagged. We show, that the algorithm can reach an accuracy of 62.2% for positive, but only 14.8% for negative utterances of opinions. We examine the cases, in which the opinion could not correctly be detected or in which the linking between the opinion statement and the aspect fails. Especially, the large gap in accuracy between positive and negative utterances is analysed.

In this paper, we discuss the work in progress of our current project focusing on opinion mining in the field of insurance business. The main purpose of this project is to improve Opinion Mining methods for the German language and optimize them with special regard to the insurance business. These improved methods make it possible to extract opinions from user-generated texts (in the insurance domain) in a better quality than today. We fetch the required text data for this study from a huge online community website for customers. There, we find a sufficient number of user reviews about insurance companies, which is necessary for our research. Besides the main purpose of this study, another aim is the development of a prototype. This could then be used to monitor the current "crowd’s opinion" about insurance products and services. For this reason and in order to understand key aspects of the domain, we collaborate with the nobisCum Deutschland GmbH, a German company offering consulting and software development services for the insurance industry. Using one data source and limited evaluation sets, we obtained first results which look promising.

We present a new phrase-based generated list of opinion bearing words and phrases for the German language. The list contains adjectives and nouns as well as adjectiveand noun-based phrases and their opinion values on a continuous range between -1 and +1. For each word or phrase two additional quality measures are given. The list was produced using a large number of product review titles providing a textual assessment and numerical star ratings from Amazon.de. As both, review titles and star ratings, can be regarded as a summary of the writers opinion concerning a product, they are strongly correlated. Thus, the opinion value for a given word or phrase is derived from the mean star rating of review titles which contain the word or phrase. The paper describes the calculation of the opinion values and the corrections which were necessary due to the so-called “J-shaped distribution” of online reviews. The opinion values obtained are amazingly accurate.

Many factors trigger migraine attacks. Weather is often reported to be one of the most common migraine triggers. However, there is little scientific evidence about the underlying mechanisms and causes. In our pilot study, we used smartphone apps and a web form to collect around 4,700 migraine messages in Germany between June 2011 and February 2012. Taking interdiurnal temperature changes as an indicator for changes in the prevailing meteorological conditions, our analyses were focused on the relationship between temperature changes and the frequency of occurrence of migraine attacks. Linear trends were fitted to the total number of migraine messages with respect to temperature changes. Statistical and systematic errors were estimated. Both increases and decreases in temperature lead to a significant increase in the number of migraine messages. A temperature increase (decrease) of 5 °C resulted in an increase of 19 ± 7 % (24 ± 8 %) in the number of migraine messages.

In this paper we present an approach to generate lists of opinion bearing phrases with their opinion values in a continuous range between -- 1 and 1. Opinion phrases that are considered include single adjectives as well as adjective-based phrases with an arbitrary number of words. The opinion values are derived from user review titles and star ratings, as both can be regarded as summaries of the user's opinion about the product under review. Phrases are organized in trees with the opinion bearing adjective as tree root. For trees with missing branches, opinion values then can be calculated using trees with similar branches but different roots. An example list is produced and compared to existing opinion lists.

This paper discusses the work in progress of the ”Migraine Radar” project. The purpose of the project is to validate or disprove the assumed correlation between migraine attacks and weather conditions, especially weather changes. There have been various medical studies on this topic, but the correlation could not be proved with sufficient statistical significance so far. Furthermore, the results of some of the studies are contradictory. For this study, data from the microblogging platform Twitter will be analyzed. Twitter messages (”tweets”) announcing currently or recently happened migraine attacks are retrieved using the Twitter API (Search-API, REST-API - Standard APIs provided by Twitter to retrieve tweet and user data). Weather data from weather information services are linked to the tweets, using the location information from Twitter. For the German language area, the results will be compared with the results obtained from a set of migraine announcements collected with the help of a web form in the same period of time. First statistics indicate that the number of migraine attacks announced in Twitter exceeds the number of cases in former classical studies by far. The project offers a wide range of possibilities to analyze Twitter messages with regard to migraine attacks. Beside the main purpose, it is also possible to analyze the distribution of migraine attacks over the weekdays or over the seasons. Furthermore an investigation of the spatial distribution of migraine attacks is possible. Instead of weather data, other information can be linked to the migraine sample as well. One example could be air pollution data.