El valor de p del test no es un ‘índice matemático’, es simplemente una frecuencia relativa / The p-value of the test is not a ‘mathematical index’, it is simply a relative frequency

Las revistas científicas más importantes en campos como medicina, biología y sociología publican reiteradamente artículos y editoriales denunciando que un gran porcentaje de médicos no entiende los conceptos básicos del análisis estadístico, lo que favorece el riesgo de cometer errores al interpretar los datos, los hace más vulnerables frente a informaciones falsas y reduce la eficacia de la investigación. Este problema se extiende a lo largo de toda su carrera profesional y se debe, en gran parte, a una enseñanza deficiente en estadística que es común en países desarrollados. En palabras de H. Halle y S. Krauss, ‘el 90% de los profesores universitarios alemanes que usan con asiduidad el valor de p de los test no entiende lo que mide ese valor’. Es importante destacar que los razonamientos básicos del análisis estadístico son similares a los que realizamos en nuestra vida cotidiana y que comprender los conceptos básicos del análisis estadístico no requiere conocimiento matemático alguno. En contra de lo que muchos investigadores creen, el valor de p del test no es un ‘índice matemático’ que nos permita concluir claramente si, por ejemplo, un fármaco es más efectivo que el placebo. El valor de p del test es simplemente un porcentaje.(AU)

Abstract. Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics – a problem that is common in developed countries. As stated by H. Halle and S. Krauss, ‘90% of German university lecturers who regularly use the p-value in tests do not understand what that value actually measures’. It is important to note that the basic reasoning of statistical analysis is similar to what we do in our daily lives and that understanding the basic concepts of statistical analysis does not require any knowledge of mathematics. Contrary to what many researchers believe, the p-value of the test is not a ‘mathematical index’ that allows us to clearly conclude whether, for example, a drug is more effective than a placebo. The p-value of the test is simply a percentage.(AU)

Harnessing the potential of African youth for transforming health research in Africa.

Africa faces a significant burden of infectious diseases, including Malaria and HIV/AIDS, along with an increasing prevalence of non-infectious diseases such as diabetes and cancer. This dual health challenge is amplified by socioeconomic difficulties, restricted access to healthcare, and lifestyle changes, thus present unique scientific needs. Effectively addressing these issues requires a skilled scientific workforce adept in comprehensive healthcare strategies. This analysis explores the critical landscape of health research in Africa, emphasizing the unique opportunity presented by the continent's youthful population, projected to reach almost 1 billion by 2050. The youth's innovative potential and fresh perspectives offer a chance to overcome development barriers in health research. Nevertheless, challenges such as under-resourced education, limited research training, inadequate mentorship, and funding difficulties persist. This paper urgently calls upon African leaders, international partners, and stakeholders to prioritize health research, mobilize funding, forge strategic partnerships, and empower the youth as essential steps to capitalize on the continent's dynamic youth for breakthrough health outcomes. Such investments are vital not just for health but for the overall economic, social, and strategic growth of the continent. Through shared responsibility and a united effort, the potential of African youth can be harnessed, leading to transformative research, improved health outcomes, and a prosperous future. This perspective represents the collective voice of passionate young researchers and advocates across Africa, calling for a new era of health research on the continent.

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. RESULTS: The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). CONCLUSIONS: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

BACKGROUND: The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. METHODS: We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. RESULTS: Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. CONCLUSION: The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

[Current regulations in the Animal Welfare Act and the significance for animal research]. / Aktuelle Regelungen im Tierschutzgesetz und Bedeutung für die tierexperimentelle Forschung.

Due to the legal implantation of the 3R principle, the number of laboratory animals decreased significantly over the past 10 years. In this article, the historical development of animal experiments over the last decade will be presented in the context of the current regulations of the Animal Welfare Act. It points out bureaucratic obstacles to the approval of animal experiments, which jeopardize Germany as a research location for both academia and industry. The article presents constructive proposals for solutions. This should be done in accordance with the DFG recommendation to ensure efficient biomedical research while maintaining the highest animal welfare standards.

A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes.

Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emphasizing the number of beneficiaries and the impact level. To evaluate clinical significance, minimal clinically important risk difference (MCIRD) is proposed based on event magnitude (EM). Within this framework, risk difference (RD) is introduced as the primary measure. To assess the performance of RD, we compared its statistical power against other measures (risk ratio, RR; odds ratio, OR; Cohen's h) in individual study scenarios, and visual information conveyance in meta-analysis scenarios. RDs maintain statistical power in comparison to other measures in individual studies. They provide clarity on the true impact of clinical interventions without compromising statistical integrity. Meta-analytic results indicate that using RDs directly enhances transparency, uncovers heterogeneity, and addresses misaligned assumptions. This approach, by quantifying clinical effectiveness under a utilitarian perspective, facilitates the applicability of research to patient care and encourages shared decision-making. The study advocates for reporting baseline risks (BRs) with RDs and recommends a standardized presentation of these statistics. In a utilitarian perspective, adopting RD as the preferred ES can foster a transparent, patient-focused research ethos. This aids in accurately presenting the magnitude and variability of treatment effects, offering a new direction in methodology.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications.

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

RecSOI: recommending research directions using statements of ignorance.

The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that draws from statements of ignorance (SOIs) found in the research literature. By building researchers' profiles based on textual elements, RecSOI generates personalized recommendations of potential research directions tailored to their interests. In addition, RecSOI provides context for the recommended SOIs, so that users can quickly evaluate how relevant the research direction is for them. In this paper, we provide an overview of RecSOI's functioning, implementation, and evaluation, demonstrating its effectiveness in guiding researchers through the vast landscape of potential research directions.

Meaningful Patient Engagement in Adolescent and Young Adult (AYA) Cancer Research: A Framework for Qualitative Studies.

Over the last two decades, patient engagement in cancer research has evolved significantly, especially in addressing the unique challenges faced by adolescent and young adult (AYA) cancer populations. This paper introduces a framework for meaningful engagement with AYA cancer patient research partners, drawing insights from the "FUTURE" Study, a qualitative study that utilizes focus groups to explore the impact of cancer diagnosis and treatment on the sexual and reproductive health of AYA cancer patients in Canada. The framework's development integrates insights from prior works and addresses challenges with patient engagement in research specific to AYA cancer populations. The framework is guided by overarching principles (safety, flexibility, and sensitivity) and includes considerations that apply across all phases of a research study (collaboration; iteration; communication; and equity, diversity, and inclusion) and tasks that apply to specific phases of a research study (developing, conducting, and translating the study). The proposed framework seeks to increase patient engagement in AYA cancer research beyond a supplementary aspect to an integral component for conducting research with impact on patients.

Demographic Analysis of Cancer Research Priorities and Treatment Correlations.

Understanding the diversity in cancer research priorities and the correlations among different treatment modalities is essential to address the evolving landscape of oncology. This study, conducted in collaboration with the European Cancer Patient Coalition (ECPC) and Childhood Cancer International-Europe (CCI-E) as part of the "UNCAN.eu" initiative, analyzed data from a comprehensive survey to explore the complex interplay of demographics, time since cancer diagnosis, and types of treatments received. Demographic analysis revealed intriguing trends, highlighting the importance of tailoring cancer research efforts to specific age groups and genders. Individuals aged 45-69 exhibited highly aligned research priorities, emphasizing the need to address the unique concerns of middle-aged and older populations. In contrast, patients over 70 years demonstrated a divergence in research priorities, underscoring the importance of recognising the distinct needs of older individuals in cancer research. The analysis of correlations among different types of cancer treatments underscored the multidisciplinary approach to cancer care, with surgery, radiotherapy, chemotherapy, precision therapy, and biological therapies playing integral roles. These findings support the need for personalized and combined treatment strategies to achieve optimal outcomes. In conclusion, this study provides valuable insights into the complexity of cancer research priorities and treatment correlations in a European context. It emphasizes the importance of a multifaceted, patient-centred approach to cancer research and treatment, highlighting the need for ongoing support, adaptation, and collaboration to address the ever-changing landscape of oncology.

Ending "domestic helicopter research".

"Helicopter research" refers to a practice where researchers from wealthier countries conduct studies in lower-income countries with little involvement of local researchers or community members. This practice also occurs domestically. In this Commentary, we outline strategies to curb domestic helicopter research and to foster equity-centered collaborations.

Measurement Invariance in Intellectual and Developmental Disability Research.

Measurement invariance (MI) is a psychometric property of an instrument indicating the degree to which scores from an instrument are comparable across groups. In recent years, there has been a marked uptick in publications using MI in intellectual and developmental disability (IDD) samples. Our goal here is to provide an overview of why MI is important to IDD researchers and to describe some challenges to evaluating it, with an eye towards nudging our subfield into a more thoughtful and measured interpretation of studies using MI.