What causes increasing and unnecessary use of radiological investigations? a survey of radiologists' perceptions

<p>Abstract</p> <p>Background</p> <p>Growth in use and overuse of diagnostic imaging significantly impacts the quality and costs of health care services. What are the modifiable factors for increasing and unnecessary use of radiological services? Various factors have been indentified, but little is known about their relative impact. Radiologists hold key positions for providing such knowledge. Therefore the purpose of this study was to obtain radiologists' perspective on the causes of increasing and unnecessary use of radiological investigations.</p> <p>Methods</p> <p>In a mailed questionnaire radiologist members of the Norwegian Medical Association were asked to rate potential causes of increased investigation volume (fifteen items) and unnecessary investigations (six items), using five-point-scales. Responses were analysed by using summary statistics and Factor Analysis. Associations between variables were determined using Students' t-test, Spearman rank correlation and Chi-Square tests.</p> <p>Results</p> <p>The response rate was 70% (374/537). The highest rated causes of increasing use of radiological investigations were: a) new radiological technology, b) peoples' demands, c) clinicians' intolerance for uncertainty, d) expanded clinical indications, and e) availability. 'Over-investigation' and 'insufficient referral information' were reported the most frequent causes of unnecessary investigations. Correlations between causes of increasing and unnecessary radiology use were identified.</p> <p>Conclusion</p> <p>In order to manage the growth in radiological imaging and curtail inappropriate investigations, the study findings point to measures that influence the supply and demand of services, specifically to support the decision-making process of physicians.</p

100 ancient genomes show repeated population turnovers in Neolithic Denmark.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales &lt;sup&gt;1-4&lt;/sup&gt; . However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution &lt;sup&gt;5-7&lt;/sup&gt; . Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet ( &lt;sup&gt;13&lt;/sup&gt; C and &lt;sup&gt;15&lt;/sup&gt; N content), mobility ( &lt;sup&gt;87&lt;/sup&gt; Sr/ &lt;sup&gt;86&lt;/sup&gt; Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use

Population genomics of post-glacial western Eurasia.

Western Eurasia witnessed several large-scale human migrations during the Holocene &lt;sup&gt;1-5&lt;/sup&gt; . Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations

Integrated bedrock model combining airborne geophysics and sparse drillings based on an artificial neural network

Cost overruns caused by unforeseen geological challenges are commonplace for large infrastructure projects. Thorough ground investigations can reduce this risk, but geotechnical drillings and laboratory test are expensive and time consuming. Airborne electromagnetics (AEM) is a low-cost geophysical method being increasingly used for geotechnical ground investigations. However, extracting engineering parameters from these complex data is challenging. We present a novel approach of extracting depth to bedrock from AEM data using artificial neural networks (ANN) and sparse drillings. Using synthetic models, we test its theoretical performance and analyse sources of error. We find that geological complexity is the main limitation on performance. We also test the algorithm on real field data from a complex geological setting. Results show that ANNs produce bedrock models that rival the accuracy of manual interpretations by experts and that are markedly more accurate than existing automated resistivity model interpretation methods. Using ANN based bedrock interpretation, one needs 2 to 3.5 times fewer geotechnical drillings (i.e., a reduction of 50–70%) in the early phases of a project compared to ground investigations using only borehole data. Further improvements may be possible with strategic planning of drilling campaigns and careful data pre-processing.publishedVersio

Integrated bedrock model combining airborne geophysics and sparse drillings based on an artificial neural network

Cost overruns caused by unforeseen geological challenges are commonplace for large infrastructure projects. Thorough ground investigations can reduce this risk, but geotechnical drillings and laboratory test are expensive and time consuming. Airborne electromagnetics (AEM) is a low-cost geophysical method being increasingly used for geotechnical ground investigations. However, extracting engineering parameters from these complex data is challenging. We present a novel approach of extracting depth to bedrock from AEM data using artificial neural networks (ANN) and sparse drillings. Using synthetic models, we test its theoretical performance and analyse sources of error. We find that geological complexity is the main limitation on performance. We also test the algorithm on real field data from a complex geological setting. Results show that ANNs produce bedrock models that rival the accuracy of manual interpretations by experts and that are markedly more accurate than existing automated resistivity model interpretation methods. Using ANN based bedrock interpretation, one needs 2 to 3.5 times fewer geotechnical drillings (i.e., a reduction of 50–70%) in the early phases of a project compared to ground investigations using only borehole data. Further improvements may be possible with strategic planning of drilling campaigns and careful data pre-processing