Measuring vaccine protection when the population is mostly vaccinated.

Objectives: This study aims to address limitations in assessing vaccine protection using the classical vaccine effectiveness (VE) measure, especially in contexts where a significant portion of the population is already vaccinated or infected.

Study Design And Setting: We propose using the adjusted number of cases (ANC) as a building block for deriving vaccine effectiveness measures. This approach accounts for biases arising from small and unrepresentative unvaccinated reference groups with incomplete data.

Protection against Omicron BA.1/BA.2 severe disease 0-7 months after BNT162b2 booster.

Following evidence of waning immunity against both infection and severe disease after 2 doses of the BNT162b2 vaccine, Israel began administering a 3rd BNT162b2 dose (booster) in July 2021. Recent studies showed that the 3rd dose provides a much lower protection against infection with the Omicron variant compared to the Delta variant and that this protection wanes quickly. However, there is little evidence regarding the protection of the 3rd dose against Omicron (BA.

Initial protection against SARS-CoV-2 omicron lineage infection in children and adolescents by BNT162b2 in Israel: an observational study.

Background: The BNT162b2 (Pfizer-BioNTech) two-dose vaccine regiment for children and the BNT162b2 third dose for adolescents were approved shortly before the SARS-CoV-2 omicron (B.1.1.

Barriers and Facilitators to the Implementation of Family-Centered Technology in Complex Care: Feasibility Study.

Background: Care coordination is challenging but crucial for children with medical complexity (CMC). Technology-based solutions are increasingly prevalent but little is known about how to successfully deploy them in the care of CMC.

Objective: The aim of this study was to assess the feasibility and acceptability of GoalKeeper (GK), an internet-based system for eliciting and monitoring family-centered goals for CMC, and to identify barriers and facilitators to implementation.

Adaptive search space pruning in complex strategic problems.

People have limited computational resources, yet they make complex strategic decisions over enormous spaces of possibilities. How do people efficiently search spaces with combinatorially branching paths? Here, we study players' search strategies for a winning move in a "k-in-a-row" game. We find that players use scoring strategies to prune the search space and augment this pruning by a "shutter" heuristic that focuses the search on the paths emanating from their previous move.

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU.

In recent years, extensive resources are dedicated to the development of machine learning (ML) based clinical prediction models for intensive care unit (ICU) patients. These models are transforming patient care into a collaborative human-AI task, yet prediction of patient-related events is mostly treated as a standalone goal, without considering clinicians' roles, tasks or workflow in depth. We conducted a mixed methods study aimed at understanding clinicians' needs and expectations from such systems, informing the design of machine learning based prediction models.

Protection following BNT162b2 booster in adolescents substantially exceeds that of a fresh 2-dose vaccine.

Israel began administering a BNT162b2 booster dose to restore protection following the waning of the 2-dose vaccine. Biological studies have shown that a "fresh" booster dose leads to increased antibody levels compared to a fresh 2-dose vaccine, which may suggest increased effectiveness. To compare the real-world effectiveness of a fresh (up to 60 days) booster dose with that of a fresh 2-dose vaccine, we took advantage of a quasi-experimental study that compares populations that were eligible to receive the vaccine at different times due to age-dependent policies.

Parent Perspectives in Shared Decision-Making for Children With Medical Complexity.

Objective: Shared decision-making (SDM) may improve outcomes for children with medical complexity (CMC). CMC have lower rates of SDM than other children, but little is known about how to improve SDM for CMC. The objective of this study is to describe parent perspectives of SDM for CMC and identify opportunities to improve elements of SDM specific to this vulnerable population.

Exploring Computational User Models for Agent Policy Summarization.

AI agents support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states. Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning.

Toward Robust policy Summarization: Extended Abstract.

AI agents are being developed to help people with high stakes decision-making processes from driving cars to prescribing drugs. It is therefore becoming increasingly important to develop "explainable AI" methods that help people understand the behavior of such agents. Summaries of agent policies can help human users anticipate agent behavior and facilitate more effective collaboration.

The more the merrier? Increasing group size may be detrimental to decision-making performance in nominal groups.

Demonstrability-the extent to which group members can recognize a correct solution to a problem-has a significant effect on group performance. However, the interplay between group size, demonstrability and performance is not well understood. This paper addresses these gaps by studying the joint effect of two factors-the difficulty of solving a problem and the difficulty of verifying the correctness of a solution-on the ability of groups of varying sizes to converge to correct solutions.

Economic games on the internet: the effect of $1 stakes.

Online labor markets such as Amazon Mechanical Turk (MTurk) offer an unprecedented opportunity to run economic game experiments quickly and inexpensively. Using Mturk, we recruited 756 subjects and examined their behavior in four canonical economic games, with two payoff conditions each: a stakes condition, in which subjects' earnings were based on the outcome of the game (maximum earnings of $1); and a no-stakes condition, in which subjects' earnings are unaffected by the outcome of the game. Our results demonstrate that economic game experiments run on MTurk are comparable to those run in laboratory settings, even when using very low stakes.