Proliferations of Scientific Medical Journals: A Burden or A Blessing

Bushra, Rabia; Aslam, Nousheen

doi:10.5001/omj.2010.100

Cited by 16 publications

(11 citation statements)

References 3 publications

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“…3 However, despite the growing number of scientific publications, allowing to reach a wider audience of readers, the authors face an unprecedented challenge when selecting which journal is the ideal journal to publish their research. [3][4][5] In this difficult decision-making, the impact factor (IF) of the journal has been one of the most widely used factors to evaluate its quality, importance, and penetration in the academic environment. 6,7 The IF is calculated by dividing the number of citations of a journal in the Journal Citation Reports (JCR) in one year by the total number of articles published in the same discipline in the journal evaluated in the previous two years.…”

Section: Introductionmentioning

confidence: 99%

“…3 No entanto, apesar do crescente número de publicações científicas, permitindo alcançar um público mais amplo de leitores, os autores enfrentam um desafio sem precedentes ao selecionar qual o periódico ideal para publicar sua pesquisa. [3][4][5] Nessa difícil tomada de decisão, o fator de impacto (FI) da revista tem sido um dos fatores mais amplamente utilizados para avaliar sua qualidade, importância e penetração no meio acadêmico. 6,7 O FI é calculado dividindo o número de citações de um periódico no Journal Citation Reports (JCR) em um ano pelo número total de artigos publicados na mesma disciplina no periódico avaliado nos dois anos anteriores.…”

Section: Introductionunclassified

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Os Autores brasileiros não citam os autores brasileiros: Nada mudou desde 1994

Giordano

Lyra

Bonadiman

et al. 2021

Rev Bras Ortop (Sao Paulo)

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Resumo Objetivo Observar o perfil de autocitações da Revista Brasileira de Ortopedia (Rev Bras Ortop) e de citações deste periódico em outras revistas médicas de ortopedia de conteúdo geral ou específico de uma determinada área de conhecimento da especialidade. Métodos Trata-se de estudo observacional transversal da frequência de autocitações e citações da Rev Bras Ortop em outros cinco periódicos médicos de ortopedia de diferentes países, todas publicadas em língua inglesa. Foram analisados os 15 últimos artigos publicados em 2020 em cada uma das seis revistas estudadas. As referências usadas em cada um delas foi avaliada para identificação do periódico em que foram publicadas originalmente. A distribuição de frequência dos quatro principais periódicos citados, sua posição e o percentual relativo ao total de citações foram observados e registrados em cada uma das seis revistas. O número de vezes em que a Rev Bras Ortop foi citada em cada um dos periódicos estrangeiros selecionados foi avaliado por meio de suas frequências absoluta e relativa. Resultados O total de citações avaliadas neste estudo foi de 2527 (variando de 386 a 486 por revista). A Rev Bras Ortop apresentou baixa taxa de autocitação (2,6%), sendo citada na própria revista na sexta posição (10 de um total de 386 referências). No período estudado, a Rev Bras Ortop não foi citada em nenhum dos outros cinco periódicos médicos incluídos no estudo (frequência absoluta 0, frequência relativa 0). Conclusão Observou-se que a Rev Bras Ortop apresenta baixa referência de si própria, com taxa de autocitação de 2,6% no período estudado, mostrando que de fato o ortopedista brasileiro não cita o ortopedista brasileiro que publica na revista. Sugerimos a elaboração e a implementação de estratégias fortes de melhora da visibilidade do periódico no cenário acadêmico-científico mundial. Além disso, é fundamental que os ortopedistas brasileiros entendam esta realidade e auxiliem direta e efetivamente em sua mudança.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionunclassified

Os Autores brasileiros não citam os autores brasileiros: Nada mudou desde 1994

Giordano

Lyra

Bonadiman

et al. 2021

Rev Bras Ortop (Sao Paulo)

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“…According to the latest National Library of Medicine statistical report, more than 1.5 million new citations were indexed in PubMed in 2020 alone [ 1 ]. This high volume of literature is fed by the publication of at least 1 new article every 26 seconds [ 2 ] and 95 clinical trials per day [ 3 ]. Nevertheless, only 1% of published clinical studies meet the criteria for high scientific quality for use in health care decisions [ 4 ], driving the need for efficient and accurate approaches to identify clinical studies that have been conducted with methodological rigor.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Abdelkader¹,

Navarro²,

Parrish³

et al. 2021

JMIR Res Protoc

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Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. Objective The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. Methods Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. Results Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. Conclusions The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. International Registered Report Identifier (IRRID) DERR1-10.2196/29398

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“…A growing number of medical articles is published every year, with a current estimated rate of at least one new article every 26 seconds [7]. The large magnitude of both the documents and the assigned topics renders automatic classification algorithms a necessity in organising and providing relevant information.…”

Section: Introductionmentioning

confidence: 99%

Priberam at MESINESP Multi-label Classification of Medical Texts Task

Cardoso¹,

Marinho²,

Mendes³

et al. 2021

Preprint

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Medical articles provide current state of the art treatments and diagnostics to many medical practitioners and professionals. Existing public databases such as MEDLINE contain over 27 million articles, making it difficult to extract relevant content without the use of efficient search engines. Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments. Classifying these articles into broader medical topics can improve the retrieval of related articles [1]. The set of medical labels considered for the MESINESP task is on the order of several thousands of labels (DeCS codes), which falls under the extreme multi-label classification problem [2]. The heterogeneous and highly hierarchical structure of medical topics makes the task of manually classifying articles extremely laborious and costly. It is, therefore, crucial to automate the process of classification. Typical machine learning algorithms become computationally demanding with such a large number of labels and achieving better recall on such datasets becomes an unsolved problem. This work presents Priberam's participation at the BioASQ task Mesinesp. We address the large multi-label classification problem through the use of four different models: a Support Vector Machine (SVM) [3], a customised search engine (Priberam Search) [4], a BERT based classifier [5], and a SVM-rank ensemble [6] of all the previous models. Results demonstrate that all three individual models perform well and the best performance is achieved by their ensemble, granting Priberam the 6-th place in the present challenge and making it the 2-nd best team.

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Proliferations of Scientific Medical Journals: A Burden or A Blessing

Cited by 16 publications

References 3 publications

Os Autores brasileiros não citam os autores brasileiros: Nada mudou desde 1994

Os Autores brasileiros não citam os autores brasileiros: Nada mudou desde 1994

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Priberam at MESINESP Multi-label Classification of Medical Texts Task

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