Learning from speaking up by learning to speak up

Action Contro

Exploring the meaning of safety in the context of health care

Action Contro

Prediction of persistent shoulder pain in general practice: Comparing clinical consensus from a Delphi procedure with a statistical scoring system

<p>Abstract</p> <p>Background</p> <p>In prognostic research, prediction rules are generally statistically derived. However the composition and performance of these statistical models may strongly depend on the characteristics of the derivation sample. The purpose of this study was to establish consensus among clinicians and experts on key predictors for persistent shoulder pain three months after initial consultation in primary care and assess the predictive performance of a model based on clinical expertise compared to a statistically derived model.</p> <p>Methods</p> <p>A Delphi poll involving 3 rounds of data collection was used to reach consensus among health care professionals involved in the assessment and management of shoulder pain.</p> <p>Results</p> <p>Predictors selected by the expert panel were: symptom duration, pain catastrophizing, symptom history, fear-avoidance beliefs, coexisting neck pain, severity of shoulder disability, multisite pain, age, shoulder pain intensity and illness perceptions. When tested in a sample of 587 primary care patients consulting with shoulder pain the predictive performance of the two prognostic models based on clinical expertise were lower compared to that of a statistically derived model (Area Under the Curve, AUC, expert-based dichotomous predictors 0.656, expert-based continuous predictors 0.679 vs. 0.702 statistical model).</p> <p>Conclusions</p> <p>The three models were different in terms of composition, but all confirmed the prognostic importance of symptom duration, baseline level of shoulder disability and multisite pain. External validation in other populations of shoulder pain patients should confirm whether statistically derived models indeed perform better compared to models based on clinical expertise.</p

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Framing and time-inconsistent preferences

Recent research on intertemporal choice (e.g., Ainslie, 1991; Herrnstein, 1990; Loewenstein &amp; Elster, 1992) exhibits several pervasive effects that are Incompatible with the basic tenets of the "rational’ or "normative" economic theory. In particular, people show time-inconsistent preferences when asked to choose between payoffs occurring at different moments in time. Following an example from Herrnstein (1990), our first experiment demonstrates such time-inconsistent preferences. Using a present time perspective the majority of subjects portrayed a positive time preference by choosing the smaller but more immediate payoff; In contrast, when a future time perspective was employed (delaying all possible outcomes by a constant duration) most subjects portrayed a negative time preference for identical payoffs. Two additional experiments tested the robustness of this effect by (1) using an isolation procedure (Kahneman and Tversky, 1979), and (2) manipulating the certainty associated with the payoffs. Our results suggest that time-inconsistent preferences, as described by Herrnstein, can be interpreted as an analog of the certainty-effect (Kahneman &amp; Tversky, 1979) in the time domain