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Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research. [ABSTRACT FROM AUTHOR]

Almost all attention and learning—in particular, most early learning—take place in social settings. But little is known of how our brains support dynamic social interactions. We recorded dual electroencephalography (EEG) from 12-month-old infants and parents during solo play and joint play. During solo play, fluctuations in infants’ theta power significantly forward-predicted their subsequent attentional behaviours. However, this forward-predictiveness was lower during joint play than solo play, suggesting that infants’ endogenous neural control over attention is greater during solo play. Overall, however, infants were more attentive to the objects during joint play. To understand why, we examined how adult brain activity related to infant attention. We found that parents’ theta power closely tracked and responded to changes in their infants’ attention. Further, instances in which parents showed greater neural responsivity were associated with longer sustained attention by infants. Our results offer new insights into how one partner influences another during social interaction. [ABSTRACT FROM AUTHOR]

A key component of working memory is the ability to remember multiple items simultaneously. To understand how the human brain maintains multiple items in memory, we examined direct brain recordings of neural oscillations from neurosurgical patients as they performed a working memory task. We analyzed the data to identify the neural representations of individual memory items by identifying recording sites with broadband gamma activity that varied according to the identity of the letter a subject viewed. Next, we tested a previously proposed model of working memory, which had hypothesized that the neural representations of individual memory items sequentially occurred at different phases of the theta/alpha cycle. Consistent with this model, the phase of the theta/alpha oscillation when stimulus-related gamma activity occurred during maintenance reflected the order of list presentation. These results suggest that working memory is organized by a cortical phase code coordinated by coupled theta/alpha and gamma oscillations and, more broadly, provide support for the serial representation of items in working memory. [ABSTRACT FROM AUTHOR]

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain. [ABSTRACT FROM AUTHOR]