Talking to strangers - A week-long intervention reduces psychological barriers to social connection

Although people derive substantial benefit from social connection, they often refrain from talking to strangers because they have pessimistic expectations about how such conversations will go (e.g., they believe they will be rejected or not know what to say). Previous research has attempted but failed to get people to realize that their concerns about talking to strangers are overblown. To reduce people’s fears about talking to strangers, we developed an intervention in which participants played a week-long scavenger hunt game that involved repeatedly finding, approaching, and talking to strangers. Compared to controls, this minimal, easily replicable treatment made people less pessimistic about the possibility of rejection and more optimistic about their conversational ability and how they would feel about the conversation —and these positive expectations persisted for at least a week after the study ended. Daily reports revealed that people’s expectations grew more positive and accurate by the day, emphasizing the importance of repeated exposure in improving people’s attitudes toward talking with strangers.

Associations With Definitive Outcomes and Clinical Benefit of Cancer Drugs at the Time of Marketing Approval and in the Postmarketing Period

Background: Most anticancer drugs are approved by regulatory agencies based on surrogate measures. This article explores the variables associated with overall survival (OS), quality of life (QoL), and substantial clinical benefit among anticancer drugs at the time of approval and in the postmarketing period. Methods: Anticancer drugs approved by the FDA between January 2006 and December 2015 and with postmarketing follow-up until April 2019 were identified. We evaluated trial-level data supporting approval and any updated OS and/or QoL data. We applied the ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) and the ASCO Value Framework (ASCO-VF) to initial and follow-up studies. Results: We found that 58 drugs were approved for 96 indications based on 96 trials. At registration, approval was based on improved OS in 39 trials (41%) and improved QoL in 16 of 45 indications (36%). Postmarketing data showed an improvement in OS for 28 of 59 trials (47%) and in QoL for 22 of 48 indications (46%). At the time of approval, 25 of 94 (27%) and 26 of 80 scorable trials (33%) met substantial benefit thresholds using the ESMO-MCBS and ASCO-VF, respectively. In the postmarketing period, 37 of 69 (54%) and 35 of 65 (54%) trials met the substantial benefit thresholds. Drugs with companion diagnostics and immune checkpoint inhibitors were associated significantly with substantial clinical benefit. Conclusions: Compared with the time of approval, more anticancer drugs showed improved OS and QoL and met the ESMO-MCBS or ASCO-VF thresholds for substantial benefit over the course of postmarketing time. However, only approximately half of the trials met the threshold for substantial benefit. Companion diagnostic drugs and immunotherapy seemed to be associated with greater clinical benefit.

Orally delivered MK-4482 inhibits SARS-CoV-2 replication in the Syrian hamster model

The COVID-19 pandemic progresses unabated in many regions of the world. An effective antiviral against SARS-CoV-2 that could be administered orally for use following high-risk exposure would be of substantial benefit in controlling the COVID-19 pandemic. Herein, we show that MK-4482, an orally administered nucleoside analog, inhibits SARS-CoV-2 replication in the Syrian hamster model. The inhibitory effect of MK-4482 on SARS-CoV-2 replication is observed in animals when the drug is administered either beginning 12 h before or 12 h following infection in a high-risk exposure model. These data support the potential utility of MK-4482 to control SARS-CoV-2 infection in humans following high-risk exposure as well as for treatment of COVID-19 patients.

Orally delivered MK-4482 inhibits SARS-CoV-2 replication in the Syrian hamster model

The COVID-19 pandemic progresses unabated in many regions of the world. An effective antiviral against SARS-CoV-2 that could be administered orally for use following high-risk exposure would be of substantial benefit in controlling the COVID-19 pandemic. Herein, we show that MK-4482, an orally administered nucleoside analog, inhibits SARS-CoV-2 replication in the Syrian hamster model. The inhibitory effect of MK-4482 on SARS-CoV-2 replication was observed in animals when the drug was administered either beginning 12 hours before or 12 hours following infection in a high-risk exposure model. These data support the potential utility of MK-4482 to control SARS-CoV-2 infection in humans following high-risk exposure as well as for treatment of COVID-19 patients.

Reassessing the net health benefit (NHB) of FDA approved cancer drugs with evolution of evidence using the American Society of Clinical Oncology Value Framework (ASCO-VF).

7011 Background: Regulatory approval of oncology drugs are often based on data presented in the primary publication of clinical trials (CT). However, clinically relevant data, such as long-term overall survival (OS) and quality of life (QOL), are often reported in subsequent publications. Therefore, this study aimed to evaluate the ASCO-VF NHB at the time of drug approval and over time as further evidence is published. Methods: All FDA approved oncology drug indications from 01/06-12/16 were reviewed to identify CTs that were scorable using the ASCO-VF version 2. Subsequent publications of included CTs relevant for scoring were identified from Web of Science with a follow-up time of 3 years from approval. Using ASCO-defined threshold scores of ≤40 for low benefit and ≥45 for substantial benefit, changes in classification of benefit were assessed at 3-years post-FDA approval. Results: We identified 57 FDA approved indications (40.4% OS, 59.6% progression-free survival (PFS) as primary endpoints) with scorable ASCO-VF CTs. Among those 57 indications, 36.8% at the time of FDA approval demonstrated substantial benefit, 10.5% demonstrated intermediate benefit, and 52.6% demonstrated low benefit. We then identified 96 subsequent publications relevant to scoring within 3-years of FDA approval, consisting of primary endpoint updates (29.2%; 14.6% OS, 12.5% PFS), secondary endpoint updates (44.8%; 16.7% OS, 7.3% PFS), new reporting of secondary endpoint (4.2% OS), safety updates (28.1%), and QOL reporting (43.8%). Upon reassessment of the NHB in subsequent publications, there was an overall change from initial classification of benefit in 36.8% of trials (17.5% became substantial, 8.8% became low, and 10.5% became intermediate). Changes in scores were mainly the result of an updated hazard ratio (35.1%), change in scoring endpoints from PFS to OS as per ASCO-VF endpoint hierarchy (8.8%), toxicity updates (57.9%), new tail of the curve bonus (12.3%), palliation bonus (14.0%), or QOL bonus (22.8%). Overall, at reassessment at 3 years post-FDA approval, 42.1% were substantial, 10.5% were intermediate, and 47.3% were low benefit. Conclusions: Only a modest proportion of FDA approved drugs have demonstrated substantial NHB at time of approval. As further evidence was published, a substantial proportion of indications have a change in classification of NHB, resulting in a small increase in the overall proportion of indications being deemed to have substantial benefit at 3 years post-approval.

Artificial Intelligence in Radiology—Ethical Considerations

Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.