Determinants of soil organic matter chemistry in maritime temperate forest ecosystems

While the influence of climate, vegetation, management and abiotic site factors on total carbon budgets and turn-over is intensively assessed, the influences of these ecosystem properties on the chemical complexity of soil organic matter (SOM) remains poorly understood. This study addresses the chemical composition of NaOH-extracted SOM from maritime temperate forest sites in Flanders (Belgium) by pyrolysis-GC/MS. The studied forests were chosen based on dominant tree species (Pinus sylvestris, Fagus sylvatica, Quercus robur and Populus spp.), soil texture and soil-moisture conditions. Differences in extractable-SOM pyrolysis products were correlated to site variables including dominant tree species, management of the woody biomass, site history, soil properties, total carbon stocks and indicators for microbial activity. Despite of a typical high intercorrelation between these site variables, the influence of the dominant tree species is prominent. The extractable-SOM composition is strongly correlated to litter quality and available nutrients. In nutrient-poor forests with low litter quality, the decomposition of relatively recalcitrant compounds (i.e. short and mid-chain alkanes/alkenes and aromatic compounds) appears hampered, causing a relative accumulation of these compounds in the soil. However, if substrate quality is favorable, no accumulations of recalcitrant compounds were observed, not even under high soil-moisture conditions. Former heathland vegetation still had a profound influence on extractable-SOM chemistry of young pine forests after a minimum of 60 year

Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between deriviation and validation data is common, the impact on the out-of-sample performance is not well studied. Using analytical and simulation approaches, we examined out-of-sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.Comment: 32 pages, 4 figure

Burnout, well-being and defensive medical practice among obstetricians and gynaecologists in the UK: cross-sectional survey study

Objectives: To determine the prevalence of burnout in doctors practising obstetrics and gynaecology, and assess the association with defensive medical practice and self-reported wellbeing. Design: Nationwide online cross-sectional survey study; December 2017-March 2018. Setting: Hospitals in the United Kingdom Participants: 5661 practising Obstetrics and Gynaecology consultants, specialty and associate specialist doctors and trainees registered with the Royal College of Obstetricians and Gynaecologists Primary and Secondary Outcome Measures: Prevalence of burnout using the Maslach Burnout Inventory and defensive medical practice (avoiding cases or procedures, overprescribing, over-referral) using a 12-item questionnaire. The odds ratios of burnout with defensive medical practice and self-reported wellbeing. Results: 3102/5661 doctors (55%) completed the survey. 3073/3102 (99%) met the inclusion criteria (1462 consultants, 1357 trainees and 254 specialty and associate specialist doctors). 1116/3073 (36%) doctors met the burnout criteria, with levels highest amongst trainees (580/1357 [43%]). 258/1116 (23%) doctors with burnout reported increased defensive practice compared to 142/1957 (7%) without (adjusted odds ratio 4.35, 95% CI 3.46 to 5.49). Odds ratios of burnout with wellbeing items varied between 1.38 and 6.37, and were highest for anxiety (3.59, 95% CI 3.07 to 4.21), depression (4.05, 95% CI 3.26 to 5.04), and suicidal thoughts (6.37, 95% CI 95% CI 3.95 to 10.7). In multivariable logistic regression, being of younger age, white or ‘other’ ethnicity, and graduating with a medical degree from the UK or Ireland had the strongest associations with burnout. Conclusions: High levels of burnout were observed in obstetricians and gynaecologists and particularly amongst trainees. Burnout was associated with both increased defensive medical practice and worse doctor wellbeing. These findings have implications for the wellbeing and retention of doctors as well as the quality of patient care, and may help to inform the content of future interventions aimed at preventing burnout and improving patient safety

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Objectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols. Study Design and Setting: We examined the effects of various scenarios of predictor measurement heterogeneity in r

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not

The effects of stand characteristics on the understory vegetation in Quercus petraea and Q. cerris dominated forests

The shelterwood system used in Hungary has many effects on the composition and structure of the herb layer. The aim of our study was to identify the main variables that affect the occurence of herbs and seedlings in Turkey oak-sessile oak (Quercus cerris and Q. petraea) stands. The study was carried out in the Bükk mountains, Hungary. 122 sampling plots were established in 50-150 year old oak forests, where we studied the species composition and structure of the understorey and overstorey. The occurence of herbs was affected by canopy closure, the heterogenity and patchiness of the stand, the slope and the east-west component of the aspect. The composition of saplings was significantly explained by the ratio of the two major oak species in the stand and the proximity of the adult plants. An important result for forest management was that sessile oaks were able to regenerate almost only where they were dominant in the overstorey

Refining Ovarian Cancer Test accuracy Scores (ROCkeTS): protocol for a prospective longitudinal test accuracy study to validate new risk scores in women with symptoms of suspected ovarian cancer.

Introduction Ovarian cancer (OC) is associated with non-specific symptoms such as bloating, making accurate diagnosis challenging: only 1 in 3 women with OC presents through primary care referral. National Institute for Health and Care Excellence guidelines recommends sequential testing with CA125 and routine ultrasound in primary care. However, these diagnostic tests have limited sensitivity or specificity. Improving accurate triage in women with vague symptoms is likely to improve mortality by streamlining referral and care pathways. The Refining Ovarian Cancer Test Accuracy Scores (ROCkeTS; HTA 13/13/01) project will derive and validate new tests/risk prediction models that estimate the probability of having OC in women with symptoms. This protocol refers to the prospective study only (phase III). Methods and analysis ROCkeTS comprises four parallel phases. The full ROCkeTS protocol can be found at http://www.birmingham.ac.uk/ROCKETS. Phase III is a prospective test accuracy study. The study will recruit 2450 patients from 15 UK sites. Recruited patients complete symptom and anxiety questionnaires, donate a serum sample and undergo ultrasound scored as per International Ovarian Tumour Analysis (IOTA) criteria. Recruitment is at rapid access clinics, emergency departments and elective clinics. Models to be evaluated include those based on ultrasound derived by the IOTA group and novel models derived from analysis of existing data sets. Estimates of sensitivity, specificity, c-statistic (area under receiver operating curve), positive predictive value and negative predictive value of diagnostic tests are evaluated and a calibration plot for models will be presented. ROCkeTS has received ethical approval from the NHS West Midlands REC (14/WM/1241) and is registered on the controlled trials website (ISRCTN17160843) and the National Institute of Health Research Cancer and Reproductive Health portfolios