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In the era of somatostatin analogues and targeted therapies, the role of chemotherapy in NET remains largely undefined. This systematic review aimed to assess the effect of chemotherapy on response rates (RR), progression-free survival (PFS), overall survival (OS) and toxicity compared to other chemotherapies/systemic therapies or best supportive care in patients with advanced or metastatic NET.Randomised controlled trials (RCTs) from 1946 to 2015 were identified from MEDLINE, EMBASE, other databases and conference proceedings. Review of abstracts, quality assessment and data abstraction were performed independently by two investigators. Meta-analyses were conducted using Mantel-Haenszel analysis with random-effects modelling.Six RCTs comparing standard streptozotocin plus 5-fluorouacil (STZ/5FU) chemotherapy to other chemotherapy regimens, and 2 comparing this to interferon (IFN) were included. Only 1 study was considered at low risk of bias. STZ/5-FU was no different to other chemotherapies in response rate [RR 0.96; 95% confidence interval (CI) 0.72-1.27], PFS (RR 0.95; CI 0.81-1.13), or OS (RR 1.03; CI 0.77-1.39). IFN may produce higher response than STZ/5FU (RR 0.20; CI 0.04-1.13), but event rates were small and survival was no different. Interferon was associated with higher overall haematological (RR 0.47; CI 0.27-0.82) and lower overall renal toxicity (RR 3.61; CI 1.24-10.51).Strong evidence is lacking in the area of chemotherapy in neuroendocrine tumors. There is currently no evidence that one chemotherapeutic regimen is significantly better than the other, nor is interferon better than chemotherapy. There is an urgent need to design RCTs comparing modern chemotherapy to other agents in NET.

Research on regional linguistic variation typically involves data collection in the field. This process itself can take up several months if not years. In the present study we demonstrate how we can use web interactives in collaboration with media outlets for a fast gathering of regional, sociolinguistic data. In collaboration with SPIEGEL ONLINE and Tagesanzeiger, we developed a web interactive that predicts users' regional backgrounds from within German-speaking Europe. More than 1.9M people have participated in the interactive, more than 770K users have provided metadata. Said metadata allowed us to capture regional variation in language as of today, which we can compare to historical survey data-enabling us to track the evolution of German in Europe over the past 40 years. We report on regional levelling of lexical variants, a process which appears to be particularly prevalent in the northern parts of German-speaking Europe. We further found an effect of (former) national and regional borders on language use. This innovative paradigm allows us to collect sociolinguistic data of an unprecedented scale-at the same time it presents significant challenges, both of which-benefits and challenges-will be discussed in this contribution.

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users' dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user's dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950. Where users disagree with the prediction, they can indicate what they consider to be their dialect's location. With this information, the 16 variables can be assessed for language change. Thanks to the playfulness of its functionality, DÄ has reached many users; our linguistic analyses are based on data from nearly 60,000 speakers. Results reveal a relative stability for phonetic variables, while lexical and morphological variables seem more prone to change. Crowdsourcing large amounts of dialect data with smartphone apps has the potential to complement existing data collection techniques and to provide evidence that traditional methods cannot, with normal resources, hope to gather. Nonetheless, it is important to emphasize a range of methodological caveats, including sparse knowledge of users' linguistic backgrounds (users only indicate age, sex) and users' self-declaration of their dialect. These are discussed and evaluated in detail here. Findings remain intriguing nevertheless: as a means of quality control, we report that traditional dialectological methods have revealed trends similar to those found by the app. This underlines the validity of the crowdsourcing method. We are presently extending DÄ architecture to other languages.