The differential diagnosis of chronic daily headaches: an algorithm-based approach

Chronic daily headaches (CDHs) refers to primary headaches that happen on at least 15 days per month, for 4 or more hours per day, for at least three consecutive months. The differential diagnosis of CDHs is challenging and should proceed in an orderly fashion. The approach begins with a search for “red flags” that suggest the possibility of a secondary headache. If secondary headaches that mimic CDHs are excluded, either on clinical grounds or through investigation, the next step is to classify the headaches based on the duration of attacks. If the attacks last less than 4 hours per day, a trigeminal autonomic cephalalgia (TAC) is likely. TACs include episodic and chronic cluster headache, episodic and chronic paroxysmal hemicrania, SUNCT, and hypnic headache. If the duration is ≥4 h, a CDH is likely and the differential diagnosis encompasses chronic migraine, chronic tension-type headache, new daily persistent headache and hemicrania continua. The clinical approach to diagnosing CDH is the scope of this review

Urban Tourism, local food cultures and authenticity

Het ervaren van de lokale gastronomie staat centraal in menig stedentrip. De relatie tussen lokale eetculturen en toerisme gaat echter verder dan dat. Ze kunnen elkaar versterken, maar toerisme kan de lokale eetcultuur ook verdringen. Een veranderende eetcultuur kan daarmee signalen van toeristificering bevatten, maar hoe werkt dit

Stedelijk toerisme, lokale eetcultuur en authenticiteit

Het ervaren van de lokale gastronomie staat centraal in menig stedentrip. De relatie tussen lokale eetculturen en toerisme gaat echter verder dan dat. Ze kunnen elkaar versterken, maar toerisme kan de lokale eetcultuur ook verdringen. Een veranderende eetcultuur kan daarmee signalen van toeristificering bevatten, maar hoe werkt dit

Where to go and what to do : Extracting leisure activity potentials from Web data on urban space

Web data is the most prominent source of information for deciding where to go and what to do. Exploiting this source for geographic analysis, however, does not come without difficulties. First, in recent years, the amount and diversity of available Web information about urban space have exploded, and it is therefore increasingly difficult to overview and exploit. Second, the bulk of information is in an unstructured form which is difficult to process and interpret by computers. Third, semi-structured sources, such as Web rankings, geolocated tags, check-ins, or mobile sensor data, do not fully reflect the more subtle qualities of a place, including the particular functions that make it attractive. In this article, we explore a method to capture leisure activity potentials from Web data on urban space using semantic topic models. We test three supervised multi-label machine learning strategies exploiting geolocated webtexts and place tags to estimate whether a given type of leisure activity is afforded or not. We train and validate these models on a manually curated dataset labeled with leisure ontology classes for the city of Zwolle, and discuss their potential for urban leisure and tourism research and related city policies and planning. We found that multi-label affordance estimation is not straightforward but can be made to work using both official web texts and user-generated content on a medium semantic level. This opens up new opportunities for data-driven approaches to urban leisure and tourism studies

Where to go and what to do: Extracting leisure activity potentials from Web data on urban space

Web data is the most prominent source of information for deciding where to go and what to do. Exploiting this source for geographic analysis, however, does not come without difficulties. First, in recent years, the amount and diversity of available Web information about urban space have exploded, and it is therefore increasingly difficult to overview and exploit. Second, the bulk of information is in an unstructured form which is difficult to process and interpret by computers. Third, semi-structured sources, such as Web rankings, geolocated tags, check-ins, or mobile sensor data, do not fully reflect the more subtle qualities of a place, including the particular functions that make it attractive. In this article, we explore a method to capture leisure activity potentials from Web data on urban space using semantic topic models. We test three supervised multi-label machine learning strategies exploiting geolocated webtexts and place tags to estimate whether a given type of leisure activity is afforded or not. We train and validate these models on a manually curated dataset labeled with leisure ontology classes for the city of Zwolle, and discuss their potential for urban leisure and tourism research and related city policies and planning. We found that multi-label affordance estimation is not straightforward but can be made to work using both official web texts and user-generated content on a medium semantic level. This opens up new opportunities for data-driven approaches to urban leisure and tourism studies