The influence of decision-making in tree ring-based climate reconstructions.
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ABSTRACT:
SUBMITTER: Buntgen U
PROVIDER: S-EPMC8184857 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Decision analysis is a tool that clinicians can use to choose an option that maximizes the overall net benefit to a patient. It is an explicit, quantitative, and systematic approach to decision making under conditions of uncertainty. In this article, we present two teaching tips aimed at helping clinical learners understand the use and relevance of decision analysis. The first tip demonstrates the structure of a decision tree. With this tree, a clinician may identify the optimal choice among complicated options by calculating probabilities of events and incorporating patient valuations of possible outcomes. The second tip demonstrates how to address uncertainty regarding the estimates used in a decision tree. We field tested the tips twice with interns and senior residents. Teacher preparatory time was approximately 90 minutes. The field test utilized a board and a calculator. Two handouts were prepared. Learners identified the importance of incorporating values into the decision-making process as well as the role of uncertainty. The educational objectives appeared to be reached. These teaching tips introduce clinical learners to decision analysis in a fashion aimed to illustrate principles of clinical reasoning and how patient values can be actively incorporated into complex decision making.
| S-EPMC2669856 | biostudies-other
Project description:ObjectiveTo assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA).Materials and methodsFrom March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups.ResultsMultivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100).ConclusionAbnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.
| S-EPMC4644760 | biostudies-literature
Project description:The data set presented represents 15 years of collection. It contains tree-ring width measurements from 64 sites of living trees and ten historical chronologies based on archaeological and construction wood up to year 572 CE, altogether 2909 tree-ring series and more than 450000 measured and cross-dated tree rings. It covers the vast territory of European Russia, including its forested northern and central parts, and the Northern Caucasus mountains. The potential use of these data include climatic reconstructions of regional and hemispheric scale, dendrochronological dating of historical and cultural wood, ecological and remote sensing studies.
| S-EPMC9237095 | biostudies-literature
Project description:The development of dendrochronological time series in order to analyze climate-growth relationships usually involves first a rigorous selection of trees and then the computation of the mean tree-growth measurement series. This study suggests a change in the perspective, passing from an analysis of climate-growth relationships that typically focuses on the mean response of a species to investigating the whole range of individual responses among sample trees. Results highlight that this new approach, tested on a larch and stone pine tree-ring dataset, outperforms, in terms of information obtained, the classical one, with significant improvements regarding the strength, distribution and time-variability of the individual tree-ring growth response to climate. Moreover, a significant change over time of the tree sensitivity to climatic variability has been detected. Accordingly, the best-responder trees at any one time may not always have been the best-responders and may not continue to be so. With minor adjustments to current dendroecological protocol and adopting an individualistic approach, we can improve the quality and reliability of the ecological inferences derived from the climate-growth relationships.
| S-EPMC3145760 | biostudies-literature
Project description:BackgroundGoal attainment of guideline-recommended low-density lipoprotein cholesterol (LDL-C) is suboptimal. Little is known about how patient factors influence physicians' treatment decision-making in hypercholesterolemia. We examined physicians' treatment recommendations in high-risk patients whose LDL-C remained uncontrolled despite statin monotherapy.MethodsPhysicians completed a questionnaire prior to randomization into period I of a two-period randomized controlled trial evaluating LDL-C goal attainment in patients whose LDL-C remained ≥100 mg/dL after 5 weeks' treatment with atorvastatin 10 mg/day (NCT01154036). Physicians' treatment recommendations were surveyed for two hypothetical and one real scenario: (1) LDL-C presumed near goal (between 100-105 mg/dL), (2) LDL-C presumed far from goal (~120 mg/dL), and (3) observed baseline LDL-C of enrolled patients. Prognostic factors considered during decision-making were identified by regression analysis. Observed lipid outcomes at the end of period I (following 6 weeks' treatment with ezetimibe 10 mg plus atorvastatin 10 mg, atorvastatin 20 mg, or rosuvastatin 10 mg) were compared with estimated LDL-C outcomes for physicians' treatment recommendations after 6 weeks (based on individual patients' pre-randomization LDL-C and expected incremental change).ResultsQuestionnaires were completed for 1,534 patients. No change in therapy, or double atorvastatin dose, were frequently recommended, even when LDL-C was far from goal (6.5% and 52.2% of patients, respectively). Double atorvastatin dose was commonly recommended in all scenarios (43-52% of patients). More intensive LDL-C-lowering regimens were recommended infrequently e.g. double atorvastatin dose and add ezetimibe only <12% in all scenarios. Overall, cardiovascular risk factors and desire to achieve a more aggressive LDL-C goal were prominent factors in decision-making for treatment. Comparison of observed and estimated LDL-C levels showed that physicians tended to overestimate the effectiveness of their recommendations.ConclusionsThis study provides insight into physicians' perspectives on clinical management of hypercholesterolemia and highlights a gap in knowledge translation from guidelines to clinical practice. The need for lower LDL-C and cardiovascular risk were key drivers in clinical decision-making, but physicians' treatment choices were more conservative than guideline recommendations, potentially resulting in poorer LDL-C reduction. When compared with actual outcomes, projected LDL-C control was better if physicians used more comprehensive strategies rather than simply doubling the statin dose.Trial registrationClinicaltrials.gov: NCT01154036.
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Project description:Motor decision-making is an essential component of everyday life which requires weighing potential rewards and punishments against the probability of successfully executing an action. To achieve this, humans rely on two key mechanisms; a flexible, instrumental, value-dependent process and a hardwired, Pavlovian, value-independent process. In economic decision-making, age-related decline in risk taking is explained by reduced Pavlovian biases that promote action toward reward. Although healthy ageing has also been associated with decreased risk-taking in motor decision-making, it is currently unknown whether this is a result of changes in Pavlovian biases, instrumental processes or a combination of both. Using a newly established approach-avoidance computational model together with a novel app-based motor decision-making task, we measured sensitivity to reward and punishment when participants (n = 26,532) made a 'go/no-go' motor gamble based on their perceived ability to execute a complex action. We show that motor decision-making can be better explained by a model with both instrumental and Pavlovian parameters, and reveal age-related changes across punishment- and reward-based instrumental and Pavlovian processes. However, the most striking effect of ageing was a decrease in Pavlovian attraction towards rewards, which was associated with a reduction in optimality of choice behaviour. In a subset of participants who also played an independent economic decision-making task (n = 17,220), we found similar decision-making tendencies for motor and economic domains across a majority of age groups. Pavlovian biases, therefore, play an important role in not only explaining motor decision-making behaviour but also the changes which occur through normal ageing. This provides a deeper understanding of the mechanisms which shape motor decision-making across the lifespan.
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Project description:Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data.
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Project description:Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world's largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960-2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960-2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982-2014. No significant changes in SOS or EOS were observed during 1960-1981. April-June and August-September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April-June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale.
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Project description:Adolescence is a period of life during which peers play a pivotal role in decision-making. The narrative of social influence during adolescence often revolves around risky and maladaptive decisions, like driving under the influence, and using illegal substances (Steinberg, 2005). However, research has also shown that social influence can lead to increased prosocial behaviors (Van Hoorn et al., 2017) and a reduction in risk-taking (Braams et al., 2019). While many studies support the notion that adolescents are more sensitive to peer influence than children or adults, the developmental processes that underlie this sensitivity remain poorly understood. We argue that one important reason for this lack of understanding is the absence of precisely formulated models. To make a first step toward formal models of social influence during adolescence, we first identify three prominent verbal models of social influence in the literature: (1) social motivation, (2) reward sensitivity, and (3) distraction. We then illustrate how these can be translated into formal models, and how such formal models can inform experimental design and help identify developmental processes. Finally, by applying our formal models to existing datasets, we demonstrate the usefulness of formalization by synthesizing different studies with seemingly disparate results. We conclude with a discussion on how formal modeling can be utilized to better investigate the development of peer influence in adolescence.
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Project description:Recent research suggests that response inhibition training can alter impulsive and compulsive behavior. When stop signals are introduced in a gambling task, people not only become more cautious when executing their choice responses, they also prefer lower bets when gambling. Here, we examined how stopping motor responses influences gambling. Experiment 1 showed that the reduced betting in stop-signal blocks was not caused by changes in information sampling styles or changes in arousal. In Experiments 2a and 2b, people preferred lower bets when they occasionally had to stop their response in a secondary decision-making task but not when they were instructed to respond as accurately as possible. Experiment 3 showed that merely introducing trials on which subjects could not gamble did not influence gambling preferences. Experiment 4 demonstrated that the effect of stopping on gambling generalized to different populations. Further, 2 combined analyses suggested that the effect of stopping on gambling preferences was reliable but small. Finally, Experiment 5 showed that the effect of stopping on gambling generalized to a different task. On the basis of our findings and earlier research, we propose that the presence of stop signals influences gambling by reducing approach behavior and altering the motivational value of the gambling outcome.
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