Sow body condition at weaning and reproduction performance in organic piglet production

The objective was to investigate the variation in backfat at weaning and its relations to reproduction results in organic sow herds in Denmark. The study included eight herds and 573 sows. The average backfat at weaning mean�13 mm; SD�4.2 mm) ranging from 10.5 to 17.3 mm among herds shows that it is possible to avoid poor body condition at weaning even with a lactation length of seven weeks or more. No main effect of backfat at weaning on reproduction performance was found, but the probability of a successful reproduction after weaning tended to decrease with decreasing backfat for first parity sows, whereas the opposite was the case for multiparous sows

Fremtidens udfordringer i økologisk og frilandssvineproduktion

I juni deltog 48 svineproducenter, rådgivere og forskere i en temadag på Hovborg Kro om økologisk svineproduktion. Temaet var "fremtidens udfordringer i økologisk og frilands svineproduktion". Dagen bød på indlæg fra fem forskningsprojekter og efterfølgende café-diskussion med fokus på miljø, dyrevelfærd, selvforsyning med protein og produktion af hangrise. Her bringes et uddrag af diskussionen

SpineData – A Danish clinical registry of people with chronic back pain

Background: Large-scale clinical registries are increasingly recognized as important resources for quality assurance and research to inform clinical decision-making and health policy. We established a clinical registry (SpineData) in a conservative care setting where more than 10,000 new cases of spinal pain are assessed each year. This paper describes the SpineData registry, summarizes the characteristics of its clinical population and data, and signals the availability of these data as a resource for collaborative research projects. Methods: The SpineData registry is an Internet-based system that captures patient data electronically at the point of clinical contact. The setting is the government-funded Medical Department of the Spine Centre of Southern Denmark, Hospital Lillebaelt, where patients receive a multidisciplinary assessment of their chronic spinal pain. Results: Started in 2011, the database by early 2015 contained information on more than 36,300 baseline episodes of patient care, plus the available 6-month and 12-month follow-up data for these episodes. The baseline questionnaire completion rate has been 93%; 79% of people were presenting with low back pain as their main complaint, 6% with mid-back pain, and 15% with neck pain. Collectively, across the body regions and measurement time points, there are approximately 1,980 patient-related variables in the database across a broad range of biopsychosocial factors. To date, 36 research projects have used data from the SpineData registry, including collaborations with researchers from Denmark, Australia, the United Kingdom, and Brazil. Conclusion: We described the aims, development, structure, and content of the SpineData registry, and what is known about any attrition bias and cluster effects in the data. For epidemiology research, these data can be linked, at an individual patient level, to the Danish population-based registries and the national spinal surgery registry. SpineData also has potential for the conduct of cohort multiple randomized controlled trials. Collaborations with other researchers are welcome

Development, validation and use of custom software for the analysis of pain trajectories

In chronic musculoskeletal conditions, the prognosis tends to be more informative than the diagnosis for the future course of the disease. Many studies have identified clusters of patients who seemingly share similar pain trajectories. In a dataset of low back pain (LBP) patients, pain trajectories have been identified, and distinct trajectory types have been defined, making it possible to create pattern recognition software that can classify patients into respective pain trajectories reflecting their condition. It has been suggested that the classification of pain trajectories may create clinically meaningful subgroups of patients in an otherwise heterogeneous population of patients with LBP. A software tool was created that combined the ability to recognise the pain trajectory of patients with a system that could create subgroups of patients based on their characteristics. This tool is primarily meant for researchers to analyse trends in large heterogeneous datasets without large losses of data. Prospective analysis of pain trajectories is not directly helpful for clinicians. However, the tool might aid in the identification of patient characteristics which have predictive capabilities of the most likely trajectory a patient might experience in the future. This will help clinicians to tailor their advice and treatment for a specific patient.</p