Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study.

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.

Development and validation of a machine-learning algorithm to predict the relevance of scientific articles within the field of teratology.

The Dutch Teratology Information Service Lareb counsels healthcare professionals and patients about medication use during pregnancy and lactation. To keep the evidence up to date, employees perform a standardized weekly PubMed query where relevant literature is identified manually. We aimed to develop an accurate machine-learning algorithm to predict the relevance of PubMed entries, thereby reducing the labor-intensive task of manually screening the articles. We fine-tuned a pre-trained natural language processing transformer model to identify relevant entries. We split 15,540 labeled entries into case-control-balanced train, validation, and test datasets. Additionally, we externally validated the model prospectively with 1288 labeled entries obtained from weekly queries after developing the model. This dataset was also independently labeled by a team of six experienced human raters to evaluate our model's performance. The validation of our machine learning model on the retrospectively collected outheld dataset obtained an area under the sensitivity-versus-specificity curve of 89.3 % (CI: 88.2- 90.4). In the prospective external validation of the model, our model classified relevant literature with a sensitivity versus specificity curve area of 87.4 % (CI: 85.0-89.8). Our model achieved a higher sensitivity than the human raters' team without sacrificing too much specificity. The team of human raters showed weak to moderate levels of agreement in their article classifications (kappa range 0.40-0.64). The human selection of the latest relevant literature is indispensable to keep the teratology information up to date. We show that automatic preselection of relevant abstracts using machine learning is possible without sacrificing the selection performance.

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).

COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.

The embedding of Medical Subject Headings (MeSH) terms has become a foundation for many downstream bioinformatics tasks. Recent studies employ different data sources, such as the corpus (in which each document is indexed by a set of MeSH terms), the MeSH term ontology, and the semantic predications between MeSH terms (extracted by SemMedDB), to learn their embeddings. While these data sources contribute to learning the MeSH term embeddings, current approaches fail to incorporate all of them in the learning process. The challenge is that the structured relationships between MeSH terms are different across the data sources, and there is no approach to fusing such complex data into the MeSH term embedding learning. In this paper, we study the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. We propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings. COS converts the corpus, ontology, and semantic predications into MeSH term sequences, merges these sequences, and learns MeSH term embeddings using the sequences. Extensive experiments on different datasets show that COS outperforms various baseline embeddings and traditional non-embedding-based baselines.

Analysis of carbon emission performance and regional differences in China's eight economic regions: Based on the super-efficiency SBM model and the Theil index.

China's carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China's eight economic regions accurately is the premise for realizing China's carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance's static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions' carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions' carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents' living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors.

Index tracking strategy based on mixed-frequency financial data.

To obtain market average return, investment managers need to construct index tracking portfolio to replicate target index. Currently, most literatures use financial data that has homogenous frequency when constructing the index tracking portfolio. To make up for this limitation, we propose a methodology based on mixed-frequency financial data, called FACTOR-MIDAS-POET model. The proposed model can utilize the intraday return data, daily risk factors data and monthly or quarterly macro economy data, simultaneously. Meanwhile, the out-of-sample analysis demonstrates that our model can improve the tracking accuracy.

COVID-19 and diabetes: Analysis of the scientific production indexed in Scopus.

BACKGROUND AND AIM: COVID-19 has affected the world population, with a higher impact among at-risk groups, such as diabetic patients. This has led to an exponential increase in the number of studies related to the subject, although their bibliometric characteristics are unknown. This article aims to characterize the world scientific production on COVID-19 and diabetes indexed in Scopus. METHODS: Articles on the subject were retrieved using a search strategy and bibliometric indicators of production, visibility, collaboration and impact were studied. RESULTS: The total scientific production was 1956 documents, which have 35086 citations and an h-index of 67. Articles published in Diabetes and Metabolic Syndrome: Clinical Research and Reviews (n = 127), as well as those by researchers from the United States (n = 498) predominated. Articles by Chinese authors (n = 314) had the highest impact according to the received citations (n = 21757). India, China and Spain are leading countries in terms of the research in which they participate. There is extensive international scientific collaboration led by China, the United States and Italy. CONCLUSION: The volume of publications on COVID-19 and diabetes and their scientific impact show the incentive that the study of these diseases represents for the scientific community worldwide.

Search methods for prognostic factor systematic reviews: a methodologic investigation.

OBJECTIVE: This study retroactively investigated the search used in a 2019 review by Hayden et al., one of the first systematic reviews of prognostic factors that was published in the Cochrane Library. The review was designed to address recognized weaknesses in reviews of prognosis by using multiple supplementary search methods in addition to traditional electronic database searching. METHODS: The authors used four approaches to comprehensively assess aspects of systematic review literature searching for prognostic factor studies: (1) comparison of search recall of broad versus focused electronic search strategies, (2) linking of search methods of origin for eligible studies, (3) analysis of impact of supplementary search methods on meta-analysis conclusions, and (4) analysis of prognosis filter performance. RESULTS: The review's focused electronic search strategy resulted in a 91% reduction in recall, compared to a broader version. Had the team relied on the focused search strategy without using supplementary search methods, they would have missed 23 of 58 eligible studies that were indexed in MEDLINE; additionally, the number of included studies in 2 of the review's primary outcome meta-analyses would have changed. Using a broader strategy without supplementary searches would still have missed 5 studies. The prognosis filter used in the review demonstrated the highest sensitivity of any of the filters tested. CONCLUSIONS: Our study results support recommendations for supplementary search methods made by prominent systematic review methodologists. Leaving out any supplemental search methods would have resulted in missed studies, and these omissions would not have been prevented by using a broader search strategy or any of the other prognosis filters tested.