Raw data were not disclosed in 95% of PubMed-indexed heart failure meta-analyses in 2021: A systematic analysis of transparency.

BACKGROUND: The rising concern of irreproducible and non-transparent studies poses a significant challenge in modern medical literature. The impact of this issue on cardiology, particularly in the subfield of heart failure, remains poorly understood. To address this knowledge gap, we assessed the quality of evidence presented in recent heart failure meta-analyses by exploring several crucial transparency indicators. METHODS: We conducted a cross-sectional study and searched PubMed for meta - analyses themed around heart failure. We included the 100 most recent publications from 2021 and investigated the presence of several indices that are associated with transparency and reproducibility. RESULTS: The vast majority of the papers did not include their raw data (95/100, 95%) nor their analytic code (99/100, 99%). Less than half (42/100, 42%) preregistered their protocol, while only 65/100 (65%) adhered to a reporting guidelines method. Bias calculation for the respective studies included in each meta - analysis was present in 83/100 (83%) papers and publication bias was measured in approximately half (56/100, 56%). CONCLUSIONS: Our study indicates that meta-analyses in the field of heart failure present important information of transparency infrequently. Therefore, reproduction and validation of their findings seems to be practically impossible.

Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significance.

The power of language to modify the reader's perception of interpreting biomedical results cannot be underestimated. Misreporting and misinterpretation are pressing problems in randomized controlled trials (RCT) output. This may be partially related to the statistical significance paradigm used in clinical trials centered around a P value below 0.05 cutoff. Strict use of this P value may lead to strategies of clinical researchers to describe their clinical results with P values approaching but not reaching the threshold to be "almost significant." The question is how phrases expressing nonsignificant results have been reported in RCTs over the past 30 years. To this end, we conducted a quantitative analysis of English full texts containing 567,758 RCTs recorded in PubMed between 1990 and 2020 (81.5% of all published RCTs in PubMed). We determined the exact presence of 505 predefined phrases denoting results that approach but do not cross the line of formal statistical significance (P < 0.05). We modeled temporal trends in phrase data with Bayesian linear regression. Evidence for temporal change was obtained through Bayes factor (BF) analysis. In a randomly sampled subset, the associated P values were manually extracted. We identified 61,741 phrases in 49,134 RCTs indicating almost significant results (8.65%; 95% confidence interval (CI): 8.58% to 8.73%). The overall prevalence of these phrases remained stable over time, with the most prevalent phrases being "marginally significant" (in 7,735 RCTs), "all but significant" (7,015), "a nonsignificant trend" (3,442), "failed to reach statistical significance" (2,578), and "a strong trend" (1,700). The strongest evidence for an increased temporal prevalence was found for "a numerical trend," "a positive trend," "an increasing trend," and "nominally significant." In contrast, the phrases "all but significant," "approaches statistical significance," "did not quite reach statistical significance," "difference was apparent," "failed to reach statistical significance," and "not quite significant" decreased over time. In a random sampled subset of 29,000 phrases, the manually identified and corresponding 11,926 P values, 68,1% ranged between 0.05 and 0.15 (CI: 67. to 69.0; median 0.06). Our results show that RCT reports regularly contain specific phrases describing marginally nonsignificant results to report P values close to but above the dominant 0.05 cutoff. The fact that the prevalence of the phrases remained stable over time indicates that this practice of broadly interpreting P values close to a predefined threshold remains prevalent. To enhance responsible and transparent interpretation of RCT results, researchers, clinicians, reviewers, and editors may reduce the focus on formal statistical significance thresholds and stimulate reporting of P values with corresponding effect sizes and CIs and focus on the clinical relevance of the statistical difference found in RCTs.

A New Bibliometric Index: The Top 100 Most Disruptive and Developmental Publications in Colorectal Surgery Journals.

BACKGROUND: A new bibliometric index called the disruption score was recently proposed to identify innovative and paradigm-changing publications. OBJECTIVE: The goal was to apply the disruption score to the colorectal surgery literature to provide the community with a repository of important research articles. DESIGN: This study is a bibliometric analysis. SETTINGS: The 100 most disruptive and developmental publications in Diseases of the Colon & Rectum, Colorectal Disease, International Journal of Colorectal Disease, and Techniques in Coloproctology were identified from a validated data set of disruption scores and linked with the iCite National Institutes of Health tool to obtain citation counts. MAIN OUTCOME MEASURES: The primary outcomes measured were the disruption score and citation count. RESULTS: We identified 12,127 articles published in Diseases of the Colon & Rectum (n = 8109), International Journal of Colorectal Disease (n = 1912), Colorectal Disease (n = 1751), and Techniques in Coloproctology (n = 355) between 1954 and 2014. Diseases of the Colon & Rectum had the most articles in the top 100 most disruptive and developmental lists. The disruptive articles were in the top 1% of the disruption score distribution in PubMed and were cited between 1 and 671 times. Being highly cited was weakly correlated with high disruption scores (r = 0.09). Developmental articles had disruption scores that were more strongly correlated with citation count (r = 0.18). LIMITATIONS: This study is subject to the limitations of bibliometric indices, which change over time. DISCUSSION: The disruption score identified insightful and paradigm-changing studies in colorectal surgery. These studies include a wide range of topics and consistently identified editorials and case reports/case series as important research. This bibliometric analysis provides colorectal surgeons with a unique archive of research that can often be overlooked but that may have scholarly significance. See Video Abstract at http://links.lww.com/DCR/B639.UN NUEVO INDICE BIBLIOMÉTRICO: LAS 100 MAS IMPORTANTES PUBLICACIONES EN INNOVACIONES DESESTABILIZADORAS Y DE DESARROLLO EN LAS REVISTAS DE CIRUGÍA COLORRECTALANTECEDENTES:Un nuevo índice bibliométrico llamado innovación desestabilizadora y de desarrollo ha sido propuesto para identificar publicaciones de vanguardia y que pueden romper paradigmas.OBJETIVO:La meta fué aplicar el índice de desestabilización a la literature en cirugía colorectal para aportar a la comunidad con un acervo importante de artículos de investigación.DISEÑO:Un análisis bibliométrico.PARAMETROS:Las 100 publicaciones mas desestabilizadores y de desarrollo en las revistas: Diseases of the Colon and Rectum, Colorectal Disease, International Journal of Colorectal Disease, y Techniques in Coloproctology se recuperaron de una base de datos validada con puntuaciones de desestabilización y se ligaron con la herramienta iCite NIH para obtener la cuantificación de citas.PRINCIPAL MEDIDA DE RESULTADO:El índice desestabilizador y la cuantificación de citas.RESULTADOS:Se identificaron 12,127 articulos publicados en Diseases of the Colon and Rectum (n = 8,109), International Journal of Colorectal Disease (n = 1,912), Colorectal Disease (n = 1,751), y Techniques in Coloproctology (n = 355) de 1954-2014. Diseases of the Colon and Rectum representó la mayoría de las publicaciones dentro de la lista de los 100 mas desestabilizadores y de desarrollo. Esta literatura desestabilizadora se encuentra en el principal 1% de la distribución de la puntuacón desestabilizadora en PubMed y se citaron de 1 a 671 veces. El ser citado con frecuencia se relacionó vagamente con las puntuaciones de desastibilización (r = 0.09). Los artículos de desarrollo tuvieron puntuaciones de desestabilización que estuvieron muy correlacionados con la cuantificación de las citas (r = 0.18).LIMITACIONES:Las sujetas a las limitaciones de los índices bibliométricos, que se modifican en el tiempo.DISCUSION:La putuación de desestabilicación identificó trabajos perspicaces, pragmáticos y modificadores de paradigmas en cirugía colorrectal. Es de interés identificar que se incluyeron una gran variedad de temas y en forma consistente editoriales, reportes de casos y series de casos que representaron una investigación importante. Este análisis bibliométrico aporta a los cirujanos colorrectales de un acervo de investigación único que puede con frecuencia pasarse por alto, y sin embargo tener una gran importancia académica. Consulte Video Resumen en http://links.lww.com/DCR/B639. (Traducción- Dr. Miguel Esquivel-Herrera).

Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM.

Adverse drug reactions (ADRs) are the undesirable effects associated with the use of a drug due to some pharmacological action of the drug. During the last few years, social media has become a popular platform where people discuss their health problems and, therefore, has become a popular source to share information related to ADR in the natural language. This paper presents an end-to-end system for modelling ADR detection from the given text by fine-tuning BERT with a highly modular Framework for Adapting Representation Models (FARM). BERT overcame the predominant neural networks bringing remarkable performance gains. However, training BERT is a computationally expensive task which limits its usage for production environments and makes it difficult to determine the most important hyperparameters for the downstream task. Furthermore, developing an end-to-end ADR extraction system comprising two downstream tasks, i.e., text classification for filtering text containing ADRs and extracting ADR mentions from the classified text, is also challenging. The framework used in this work, FARM-BERT, provides support for multitask learning by combining multiple prediction heads which makes training of the end-to-end systems easier and computationally faster. In the proposed model, one prediction head is used for text classification and the other is used for ADR sequence labeling. Experiments are performed on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets. The proposed model is compared with the baseline models and state-of-the-art techniques, and it is shown that it yields better results for the given task with the F-scores of 89.6%, 97.6%, 84.9%, and 95.9% on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets, respectively. Moreover, training time and testing time of the proposed model are compared with BERT's, and it is shown that the proposed model is computationally faster than BERT.

Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discoveries. Current platforms available for the distribution of scientific and clinical research data and information include preprint repositories and traditional peer-reviewed journals. In recent times, social media has emerged as a helpful platform to share scientific and medical discoveries. OBJECTIVE: This study aimed to comparatively analyze activity on social media (specifically, Twitter) and that related to publications in the form of preprint and peer-reviewed journal articles in the context of COVID-19 and gastroenterology during the early stages of the COVID-19 pandemic. METHODS: COVID-19-related data from Twitter (tweets and user data) and articles published in preprint servers (bioRxiv and medRxiv) as well as in the PubMed database were collected and analyzed during the first 6 months of the pandemic, from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ-specific social media trends were compared to preprint and publication activity. Any relationship between Twitter activity and preprint articles published and that between Twitter activity and PubMed articles published overall, by organ system, and by geographic location were identified using Spearman's rank-order correlation. RESULTS: Over the 6-month period, 73,079 tweets from 44,609 users, 7164 journal publications, and 4702 preprint publications were retrieved. Twitter activity (ie, number of tweets) peaked in March 2020, whereas preprint and publication activity (ie, number of articles published) peaked in April 2020. Overall, strong correlations were identified between trends in Twitter activity and preprint and publication activity (P<.001 for both). COVID-19 data across the three platforms mainly concentrated on pulmonology or critical care, but when analyzing the field of gastroenterology specifically, most tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal gastroenterology. Furthermore, there were significant positive associations between trends in Twitter and publication activity for all gastroenterology topics (luminal gastroenterology: P=.009; hepatology and inflammatory bowel disease: P=.006; gastrointestinal endoscopy: P=.007), except pancreatology (P=.20), suggesting that Twitter activity did not correlate with publication activity for this topic. Finally, Twitter activity was the highest in the United States (7331 tweets), whereas PubMed activity was the highest in China (1768 publications). CONCLUSIONS: The COVID-19 pandemic has highlighted the potential of social media as a vehicle for disseminating scientific information during a public health crisis. Sharing and spreading information on COVID-19 in a timely manner during the pandemic has been paramount; this was achieved at a much faster pace on social media, particularly on Twitter. Future investigation could demonstrate how social media can be used to augment and promote scholarly activity, especially as the world begins to increasingly rely on digital or virtual platforms. Scientists and clinicians should consider the use of social media in augmenting public awareness regarding their scholarly pursuits.

ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions.

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.

Interdisciplinary influences in headache literature: A network citation analysis of PubMed Central articles.

BACKGROUND: Non-headache literature inevitably influences headache research, but the way this interdisciplinary interaction occurs has seldom been evaluated. OBJECTIVE: Utilizing network analysis techniques within the PubMed Central (PMC) database, we illustrate a novel method by which to identify and characterize the important non-headache literature with significant impact within the headache world. METHODS: Using the National Center for Biotechnology Information E-utilities application programing interface and custom backend software, all PMC articles containing the words "headache(s)" and/or "migraine(s)" in the title were identified. This generated a list of "seed articles" to represent the body of primary headache literature. Articles referenced by the seeds were then found, generating the list of articles with one degree of separation from the seeds (first-degree neighbors). This was iterated twice more to find the second- and third-degree neighbors. A directed network graph was generated for each level of separation using these articles and their referential connections. The hyperlink-induced topic search (HITS) and PageRank algorithms were used on these graphs to find the top 50 articles in the network (hub and authority rank via HITS, general rank via PageRank). Removing seed articles from the ranked lists left the influential non-headache articles at each level of separation. RESULTS: We extracted 6890 seed articles. The first-, second-, and third-degree models contained 16,451, 105,496, and 431,748 articles, respectively. As expected, most first-degree neighbors were part of the seed group (headache literature). Using HITS, at the second degree, only two seed articles were found in the top 50 hubs (none in the authorities); the majority of non-seed articles were basic neuroscience, involving ion channel function or cell signaling. At the third degree, there were no seeds and all articles involved imaging/structure of brain connectivity networks. PageRank gave more varied results, with 35/50 second-degree articles being seeds, and the remainder a mixture of articles describing rating scales (3), epidemiology/disease burden (3), basic statistical/trial methods (3), and mixed basic science (6). At the third degree, five were seeds; non-seed articles were represented heavily by genomic mapping studies, brain connectivity networks, and ion channel/neurotransmitter studies. CONCLUSION: This work demonstrates the value of network citation analysis in the identification of interdisciplinary influences on headache medicine. Articles found with this technique via HITS identified and grouped basic science applicable to headache medicine at the molecular scale (ion channels/transmitters), and whole-brain scale (connectivity networks). Both groups have direct clinical correlates, with the former implicating pharmacological targets, and the latter implicating functional neuroanatomy and pathophysiology of various headache disorders. Likely, in-depth analysis of the whole network (rather than the top 50) would reveal further clusters where the relationship to headache may not be as immediately obvious. This may not only help to guide ongoing work, but also identify new targets where seemingly unrelated work may have future applications in headache management.

Quantifying the scientific interest in surgical training and education: numerical evidence of a PubMed analysis.

The scientific interest (SI) for a given field can be ascertained by quantifying the volume of published research. We quantified the SI in surgical education to clarify the extent of worldwide efforts on this crucial factor required to improve health-care systems. A set of Medical Subject Headings (MeSH) was defined for the PubMed search. The number of Pubmed Indexed Papers (nPIP) relevant to the SI was extracted from database conception to December 2016 and their distribution and evolution by country were analyzed at 10-year intervals. Population Adjusted Index (PAI) and Medical School Adjusted Index (MSAI) analyses were performed for countries with the nPIP > 30. We identified 51,713 articles written in 33 different languages related to surgical education; 87.6% of these were written in English. General surgery was the leading surgical specialty. The overall nPIP doubled every 10 years from 1987 (from 6009 to 13,501, to 26,272) but stabilized at 3707, 3800 and 3433 in the past 3 years, respectively. The PAI and MSAI analyses showed that the USA, United Kingdom, New Zealand, Canada, Australia and Ireland are top producers of published research in surgical education, constituting a combined 62.88% of the nPIP. Our quantification of the change in SI in surgical education and training gives a clear picture of evolution, efforts and leadership worldwide over time. This picture mirrors an international academic society that should encourage all those involved in surgical education to improve efforts in educational research.

Using Embase as a supplement to PubMed in Cochrane reviews differed across fields.

OBJECTIVE: Medline/PubMed is often first choice for health science researchers when doing literature searches. However, Medline/PubMed does not cover the health science research literature equally well across specialties. Embase is often considered an important supplement to Medline/PubMed in health sciences. The present study analyzes the coverage of Embase as a supplement to PubMed, and the aim of the study is to investigate if searching Embase can compensate for low PubMed retrieval. STUDY DESIGN AND SETTING: The population in this study is all the included studies in all Cochrane reviews from 2012 to 2016 across the 53 Cochrane groups. The analyses were performed using two units of analysis (study and publication). We are examining the coverage in Embase of publications and studies not covered by PubMed (25,119 publications and 9,420 studies). RESULTS: The results showed that using Embase as a supplement to PubMed resulted in a coverage of 66,994 publications out of 86,167 and a coverage rate of 77.7, 95% CI [75.05, 80.45] of all the included publications. Embase combined with PubMed covered 48,326 out of 54,901 studies and thus had a coverage rate of 88.0%, 95% CI [86.2, 89.9] of studies. The results also showed that supplementing PubMed with Embase increased coverage of included publications by 6.8 percentage points, and the coverage of studies increased by 5.5 percentage points. Substantial differences were found across and within review groups over time. CONCLUSION: The included publications and studies in some groups are covered considerably better by supplementing with Embase, whereas in other groups, the difference in coverage is negligible. However, due to the variation over time, one should be careful predicting the benefit from supplementing PubMed with Embase to retrieve relevant publications to include in a review.

LitCovid: an open database of COVID-19 literature.

Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

Creating enriched training sets of eligible studies for large systematic reviews: the utility of PubMed's Best Match algorithm.

INTRODUCTION: Solutions like crowd screening and machine learning can assist systematic reviewers with heavy screening burdens but require training sets containing a mix of eligible and ineligible studies. This study explores using PubMed's Best Match algorithm to create small training sets containing at least five relevant studies. METHODS: Six systematic reviews were examined retrospectively. MEDLINE searches were converted and run in PubMed. The ranking of included studies was studied under both Best Match and Most Recent sort conditions. RESULTS: Retrieval sizes for the systematic reviews ranged from 151 to 5,406 records and the numbers of relevant records ranged from 8 to 763. The median ranking of relevant records was higher in Best Match for all six reviews, when compared with Most Recent sort. Best Match placed a total of thirty relevant records in the first fifty, at least one for each systematic review. Most Recent sorting placed only ten relevant records in the first fifty. Best Match sorting outperformed Most Recent in all cases and placed five or more relevant records in the first fifty in three of six cases. DISCUSSION: Using a predetermined set size such as fifty may not provide enough true positives for an effective systematic review training set. However, screening PubMed records ranked by Best Match and continuing until the desired number of true positives are identified is efficient and effective. CONCLUSIONS: The Best Match sort in PubMed improves the ranking and increases the proportion of relevant records in the first fifty records relative to sorting by recency.

Exploring food contents in scientific literature with FoodMine.

Thanks to the many chemical and nutritional components it carries, diet critically affects human health. However, the currently available comprehensive databases on food composition cover only a tiny fraction of the total number of chemicals present in our food, focusing on the nutritional components essential for our health. Indeed, thousands of other molecules, many of which have well documented health implications, remain untracked. To explore the body of knowledge available on food composition, we built FoodMine, an algorithm that uses natural language processing to identify papers from PubMed that potentially report on the chemical composition of garlic and cocoa. After extracting from each paper information on the reported quantities of chemicals, we find that the scientific literature carries extensive information on the detailed chemical components of food that is currently not integrated in databases. Finally, we use unsupervised machine learning to create chemical embeddings, finding that the chemicals identified by FoodMine tend to have direct health relevance, reflecting the scientific community's focus on health-related chemicals in our food.

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates.

An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19. METHODS: Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and re-searched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports. RESULTS: After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and June 2020. The meta-analysis demonstrated a point estimate of IFR of 0.68% (0.53%-0.82%) with high heterogeneity (p < 0.001). CONCLUSION: Based on a systematic review and meta-analysis of published evidence on COVID-19 until July 2020, the IFR of the disease across populations is 0.68% (0.53%-0.82%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents a completely unbiased point estimate. It is likely that, due to age and perhaps underlying comorbidities in the population, different places will experience different IFRs due to the disease. Given issues with mortality recording, it is also likely that this represents an underestimate of the true IFR figure. More research looking at age-stratified IFR is urgently needed to inform policymaking on this front.

PubMed-Indexed Research Productivity of Students Matching at Top Urology Programs: 2017-2020.

OBJECTIVE: To evaluate the number of PubMed-indexed research projects of medical students matching at top-ranked urology programs as compared to the average publications reported in the Electronic Residency Applicant Service (ERAS). METHODS: Doximity Residency Navigator was used to generate the top 50 residency programs when sorted by reputation. Residents were then found using program websites. PubMed was queried for peer-reviewed publications of incoming interns through post graduate year 3 residents as of February 2020. All PubMed-indexed research was recorded before September 15th of the residents' fourth year of medical school. We recorded the number of publications, first/last author publications, and urology-specific publications. RESULTS: The average number of publications across all 4 years was 2.38 ± 4.19. The average for urology-specific publications was 1.05 ± 3.19 and for first/last author publications was 0.80 ± 1.77. Most matched applicants had at least one PubMed-indexed publication (61.2%) and having over 3 placed them in the 75th percentile. It is uncommon for students to have urology specific or first/last author publications (34.0%, 36.5%). Top 10 programs matched applicants with significantly more research in each of the aforementioned categories and as program reputation declined, so did the publications of the applicants they matched. CONCLUSION: Most research that matched urology applicant's report in ERAS is not PubMed Indexed. Most had at least one PubMed-indexed publication by the time they submitted ERAS and those at top programs had more. It would be helpful to students and faculty advisors if ERAS published research metrics for matched and unmatched applicants separating PubMed-indexed work from posters and presentations.

Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018.

BACKGROUND Pediatric liver transplantation is used to treat children with end-stage liver disease. This study explored the research hotspots and bibliometric characteristics of pediatric liver transplantation through a variety of bibliometric analysis software. We conducted hotspot analysis to help determine important directions for future scientific research. MATERIAL AND METHODS The study samples were articles related to pediatric liver transplantation published in PubMed in the past 5 years. The high-frequency keywords are extracted by BICOMB software, and then a binary matrix and a common word matrix were constructed. Gcluto software was used to perform double-clustering and visual analysis on high-frequency words, and then we obtained hot area classification. Strategic coordinates are constructed using Excel. Citespace and VOSviewer software are used for further analysis and bibliometric data visualization. RESULTS A total of 36 high-frequency words were found in the 4118 studies. A peak map was drawn through double-cluster analysis. Biclustering analysis was used to calculate the concentricity and density of each hotspot. We obtained the top 10 countries/regions engaged in pediatric liver transplantation research. VOSviewer was used to visualize the co-author map. CONCLUSIONS We found 5 clusters and 7 aspects for pediatric liver transplantation. Additionally, calculation results showed that post-transplant lymphoproliferative disorder in pediatric patients and outcomes of multivisceral transplantation seem very promising. This conclusion is of great value for future exploratory research.

Current trends in cancer immunotherapy: a literature-mining analysis.

Cancer immunotherapy is a rapidly growing field that is completely transforming oncology care. Mining this knowledge base for biomedically important information is becoming increasingly challenging, due to the expanding number of scientific publications, and the dynamic evolution of this subject with time. In this study, we have employed a literature-mining approach that was used to analyze the cancer immunotherapy-related publications listed in PubMed and quantify emerging trends. A total of 93,033 publications published in 5055 journals have been retrieved, and 141 meaningful topics have been identified, which were further classified into eight distinct categories. Statistical analysis indicates a mean annual increase in the number of published papers of approximately 8% in the last 20 years. The research topics that exhibited the highest trends included "immune checkpoint inhibitors," "tumor microenvironment," "HPV vaccination," "CAR T-cells," and "gene mutations/tumor profiling." The top identified cancer types included "lung," "colorectal," and "breast cancer," and a shift in popularity from hematological to solid tumors was observed. As regards clinical research, a transition from early phase clinical trials to randomized control trials was recorded, indicating that the field is entering a more advanced phase of development. Overall, this mining approach provided an unbiased analysis of the cancer immunotherapy literature in a time-conserving and scale-efficient manner.

Mean age and gender distribution of patients with major mental disorders participating in clinical trials.

The mean age and gender distribution of patients seeking help for mental disorders have not yet been investigated systematically. Epidemiological surveys can provide data on gender distribution of disorders and an age range in which a disorder is most frequent, but do not offer data on the average help-seeking patient, and they are usually conducted by lay interviewers with non-clinical subjects. However, this information on age and gender can be simply extracted from randomized clinical trials (RCTs) in which consecutive clinical patients are included. As it can be assumed that the average patient tends to participate in a clinical trial when her/his illness severity has reached its highpoint, the mean age of patients in RCTs is a good estimator of the peak severity of a disorder. In RCTs, diagnoses are made by psychiatrists and only clinical patients fulfilling a minimum degree of severity are included. From 10.465 records found by electronic and hand search, we extracted 832 eligible RCTs with 151,336 patients with the 19 most relevant mental disorders. We provide a table with the mean age, standard deviation and gender distributions of all major mental disorders. These results can be used in scientific articles and educational materials and can help health care providers or researchers planning treatment programs. Patients can be informed about the natural course of the disorder. By determining the reasons why some disorders occur predominantly in a certain age or have an unbalanced gender distribution information the aetiology of these disorders may further be elucidated.