Federal Funding and Citation Metrics of US Biomedical Researchers, 1996 to 2022

K-Means clustering of dermatology journals: comparing the distribution of "free-to-publish" and "pay-to-publish" models

This study investigates the impact of Free-to-Publish (F2P) versus Pay-to-Publish (P2P) models in dermatology journals, focusing on their differences in terms of journal metrics, Article Processing Charges (APCs), and Open Access (OA) status. Utilizing k-means clustering, the research evaluates dermatology journals based on SCImago Journal Rankings (SJR), H-Index, and Impact Factor (IF), and examines the correlation between these metrics, APCs, and OA status (Full or Hybrid). Data from the SCImago Journal Rank and Journal Citation Report databases were used, and metrics from 106 journals were normalized and grouped into three tiers.The study reveals a higher proportion of F2P journals, especially in higher-tier journals, indicating a preference for quality-driven research acceptance. Conversely, a rising proportion of P2P journals in lower tiers suggests potential bias towards the ability to pay. This disparity poses challenges for researchers from less-funded institutions or those early in their careers. The study also finds significant differences in APCs between F2P and P2P journals, with hybrid OA being more common in F2P.Conclusively, the study highlights the disparities in dermatology journals between F2P and P2P models and underscores the need for further research into authorship demographics and institutional affiliations in these journals. It also establishes the effectiveness of k-means clustering as a standardized method for assessing journal quality, which can reduce reliance on potentially biased individual metrics.

The landscape of biomedical research funding in Brazil: a current overview

OBJECTIVE

The objective of this narrative review is to discuss the current state of research funding in Brazil.

MATERIALS AND METHODS

This study is based on the most recent edition of the course Funding for Research and Innovation in the University of Sao Paulo School of Medicine which was a three-day course with 12 hours of instruction. The course brought together leading experts in the field to comprehensively discuss the current state of research funding in Brazil. Each speaker provided a presentation on a specific topic related to research funding. After the workshop, speakers assembled relevant topics in this manuscript.

RESULTS

collaborative research is critical for securing research funding. It optimizes proposal competitiveness, amplifies societal impact, and manages risks effectively. As such, fostering and supporting these collaborations is paramount for both researchers and funding agencies. To maintain the highest integrity in research, investigators involved in these collaborations must disclose any relationships that could potentially influence the outcomes or interpretation of their projects.

CONCLUSIONS

In Brazil, the mainstay of research funding stems from public entities, with agencies such as CNPq, CAPES, and state bodies like FAPESP, FAPERJ, FAPEMIG and others at the forefront. Concurrently, industry funding offers viable pathways, especially through industry-sponsored studies, investigator-led projects, and collaborative initiatives. The Brazilian funding landscape is further enriched by innovative platforms, including crowdfunding and the contributions of institutions like the Serrapilheira Institute. Internationally, esteemed organizations such as the National Institutes of Health (NIH) and the Bill & Melinda Gates Foundation stand out as potential funders.

Impact of medical technologies may be predicted using constructed graph bibliometrics

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research. Random forest classification models were trained using 28 bibliometric features calculated from a dataset of 1,485,958 publications in medicine to retrospectively predict whether a publication would become high-impact. For each random forest model, the balanced accuracy score was above 0.95 and the area under the receiver operating characteristic curve was above 0.99. The high performance of high impact research prediction using our proposed models show that machine learning technologies are promising algorithms that can support funding decision-making for medical research.

Efficacy of continuous positive airway pressure on NT-pro-BNP in obstructive sleep apnea patients: a meta-analysis

BACKGROUND

N-terminal probrain natriuretic peptide (NT-pro-BNP) and BNP are well-known markers for the diagnosis and prognostic of heart failure. Until now, it was not clear whether BNP levels are influenced by events occurring within Obstructive sleep apnea-hypopnea syndrome (OSAHS) with continuous positive airway pressure (CPAP).

METHODS

A thorough search in PubMed, EMBASE, Google Scholar, and Web of Science databases up to October 24, 2022, and a meta-analysis aimed to explore further accurate estimates of the effects of BNP on OSAHS after CPAP treatment to assess the strength of the evidence.

RESULTS

The forest plot outcome indicated that CPAP therapy did not change the BNP level in patients with OSAHS, with a weighted mean difference (WMD) of -0.47 (95% CI: -1.67 to 2.62; P = 0.53] based on the random effect model because of high significant heterogeneity (I² = 80%) among the studies. Subgroup analysis also explored the changes in BNP levels in patients with OSAHS. Begg's test (P = 0.835) and Egger's test (P = 0.245) suggested significant negative publication bias.

CONCLUSION

Our meta-analysis suggests that CPAP therapy does not change the BNP level in patients with OSAHS; therefore, it is not accurate to use BNP level as an index to evaluate heart function in patients with OSAHS, but more related research should be conducted.