Search

Background: Various prognostic prediction models exist for evaluating the risk of nausea and vomiting in the postoperative period (PONV). So far, no systematic comparison of these prognostic scores is available. Method: A systematic literature search was carried out in seven medical databases to find publications on prognostic PONV models. Identified scores were assessed against prospectively defined quality criteria, including generalizability, validation and clinical relevance of the models. Results: The literature search revealed 62 relevant publications with a total of 81,834 patients which could be assigned to 8 prognostic models. The simplified Apfel score performed best, primarily because it was extensively validated. The Van den Bosch score and Sinclair score tied for second place. The simplified Koivuranta score was in third place. Conclusion: The qualitative analysis highlights the strengths and weaknesses of each prediction system based on predetermined standardized quality criteria. [ABSTRACT FROM AUTHOR]

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Copyright of Transfer: Zeitschrift für Kommunikation & Markenmanagement is the property of Deutsche Werbewissenschaftliche Gesellschaft and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of HeilberufeSCIENCE is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Rudolf‐Fettweis hydropower plant, owned and operated by EnBW Energie Baden‐Württemberg AG, is situated in the Murg valley in the Northern Black Forest. The construction of a new underground Lower Basin is planned to upgrade the existing hydropower station. The in‐situ rock mass consists of Forbach Granite with local weakness zones, as encountered in the boreholes. Considering the extent of the project area, a 3D geological prediction model was used to allow a better interpretation of the spatial relationships of weakness zones in the rock mass and to optimize the orientation of the underground caverns. Adverse effects for the public were counteracted at a very early stage, so that the project received a high level of public acceptance. The present paper describes the current state of the project. [ABSTRACT FROM AUTHOR]

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Copyright of Der Unfallchirurg is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Parodontologie: die Zeitschrift für die Praxis (Berlin, Germany) is the property of Quintessenz Verlags GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this review article 78 evaluable globally accessible articles regarding the influence of heat, drought, heavy rain, flooding, black frost, early frost, late frost, extreme frost, storm, hail, radiation and wet snow have on significant plant pests - namely fungi, insects and viruses - infesting wheat, barley and maize are summarized and discussed. The influence of other extremes and/or plant pests wasn't backed up enough by research, hence it wasn't evaluated. Sufficient evaluable literature was available for the influence drought and heat have on fungal infections, especially Fusarium and Aspergillus species and various species of aphids and the damage caused by them, including the mycotoxin levels in the grains of wheat and maize. As statements obtained from literature are partly contradicting and overall lacking information, a generalization of results is impossible. Suggestions for further research, such as the obtainment of primary data concerning temperature extremes, increased night time temperatures and their implementation in existing simulation and prediction models are made. [ABSTRACT FROM AUTHOR]

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Copyright of Der Diabetologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Diabetologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Diabetologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Methods of artificial intelligence (AI) have found applications in many fields of medicine within the last few years. Some disciplines already use these methods regularly within their clinical routine. However, the fields of application are wide and there are still many opportunities to apply these new AI concepts. This review article gives an insight into the history of AI and defines the special terms and fields, such as machine learning (ML), neural networks and deep learning. The classical steps in developing AI models are demonstrated here, as well as the iteration of data rectification and preparation, the training of a model and subsequent validation before transfer into a clinical setting are explained. Currently, musculoskeletal disciplines implement methods of ML and also neural networks, e.g. for identification of fractures or for classifications. Also, predictive models based on risk factor analysis for prevention of complications are being initiated. As non-union in bone is a rare but very complex disease with dramatic socioeconomic impact for the healthcare system, many open questions arise which could be better understood by using methods of AI in the future. New fields of research applying AI models range from predictive models and cost analysis to personalized treatment strategies., (© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)