Tracing the path of 37,050 studies into practice across 18 specialties of the 2.4 million published between 2011-2020
- Harvard Medical School, Canada
- University of Toronto, Canada
Abstract
The absence of evidence to assess treatment efficacy partially underpins the unsustainable expenditure of the US healthcare system; a challenge exacerbated by a limited understanding of the factors influencing the translation of clinical research into practice. Leveraging a dataset of >10,000 UpToDate articles, sampled every 3 months between 2011-2020, we trace the path of research (37,050 newly added articles from 887 journals) from initial publication to the point-of-care, compared to the 2.4 million uncited studies published during the same time window across 18 medical specialties. Our analysis reveals substantial variation in how specialties prioritize/adopt research, with regards to fraction of literature cited (0.4%-2.4%) and quality-of-evidence incorporated. In 9 of 18 specialties, less than 1 in 10 clinical trials are ever cited. Further, case reports represent one of the most cited article types in 12 of 18 specialties, comprising nearly a third of newly-added references for some specialties (e.g., dermatology). Anesthesiology, cardiology, critical care, geriatrics, internal medicine, and oncology tended to favor higher-quality evidence. By modelling citations as a function of NIH department-specific funding, we estimate the cost of bringing one new clinical citation to the point-of-care as ranging from thousands to tens of thousands of dollars depending on specialty. The success of a subset of specialties in incorporating a larger proportion of published research, as well as high(er) quality of evidence, demonstrates the existence of translational strategies that should be applied more broadly. In addition to providing a baseline for monitoring the efficiency of research investments, we also describe new 'impact' indices to assess the efficacy of reforms to the clinical scientific enterprise.
Data availability
We used the citation lists of all UpToDate articles published between 2011-2020. While all these citation lists are/were publicly available, we recognize the amount of work and effort required to collate and pre-process this data. As such, we have made publicly available the entire dataset used in this analysis to all readers at: https://www.8mlabs.org/uptodate/rawdataset
Article and author information
Author details
Funding
The authors declare that there was no funding for this work.
Copyright
© 2023, Abdalla et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 509
- views
-
- 77
- downloads
-
- 1
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Tracing the path of 37,050 studies into practice across 18 specialties of the 2.4 million published between 2011-2020
eLife 12:e82498.
https://doi.org/10.7554/eLife.82498
Further reading
-
- Medicine
Background:
Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a severe and deadly adverse event following ERCP. The ideal method for predicting PEP risk before ERCP has yet to be identified. We aimed to establish a simple PEP risk score model (SuPER model: Support for PEP Reduction) that can be applied before ERCP.
Methods:
This multicenter study enrolled 2074 patients who underwent ERCP. Among them, 1037 patients each were randomly assigned to the development and validation cohorts. In the development cohort, the risk score model for predicting PEP was established via logistic regression analysis. In the validation cohort, the performance of the model was assessed.
Results:
In the development cohort, five PEP risk factors that could be identified before ERCP were extracted and assigned weights according to their respective regression coefficients: –2 points for pancreatic calcification, 1 point for female sex, and 2 points for intraductal papillary mucinous neoplasm, a native papilla of Vater, or the pancreatic duct procedures (treated as ‘planned pancreatic duct procedures’ for calculating the score before ERCP). The PEP occurrence rate was 0% among low-risk patients (≤0 points), 5.5% among moderate-risk patients (1–3 points), and 20.2% among high-risk patients (4–7 points). In the validation cohort, the C statistic of the risk score model was 0.71 (95% CI 0.64–0.78), which was considered acceptable. The PEP risk classification (low, moderate, and high) was a significant predictive factor for PEP that was independent of intraprocedural PEP risk factors (precut sphincterotomy and inadvertent pancreatic duct cannulation) (OR 4.2, 95% CI 2.8–6.3; p<0.01).
Conclusions:
The PEP risk score allows an estimation of the risk of PEP prior to ERCP, regardless of whether the patient has undergone pancreatic duct procedures. This simple risk model, consisting of only five items, may aid in predicting and explaining the risk of PEP before ERCP and in preventing PEP by allowing selection of the appropriate expert endoscopist and useful PEP prophylaxes.
Funding:
No external funding was received for this work.
-
- Medicine
Estrogen significantly impacts women’s health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. Therefore, an ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX +E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. Based on the above research content, we successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. Overall, this current research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of L-AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.
