Medical publishing is critical for advancing science and improving global health, but systemic challenges hinder progress. High costs, restricted access, and profit-driven practices exclude underfunded researchers, particularly in low- and middle-income countries. Bias toward positive results, slow peer review, and the pressures of the “publish or perish” culture compromise research integrity and delay discoveries. Combined with the reproducibility crisis and predatory journals, these issues exacerbate inequities and erode trust in research. Reform is essential to ensure accessibility, transparency, and equity. By treating knowledge as a public good, we can create a fairer publishing system that advances science and benefits all.

Preprints are revolutionizing the dissemination of medical research by allowing rapid sharing of findings before peer review. Originating from practices in physics, preprints have gained traction in medicine, particularly highlighted during the COVID-19 pandemic. They are posted on servers like medRxiv, facilitating open access and encouraging global collaboration. While they offer benefits such as increased visibility, faster dissemination, and enhanced collaboration, they also pose risks including misinformation and lack of peer review. The future of preprints looks promising, with potential for better integration with traditional publishing and innovative peer review models, but requires careful navigation to maintain scientific integrity.

The blog post discusses types of statistical errors in medical research, focusing on Type 1 (false positives) and Type 2 (false negatives) errors. It explains the importance of the null hypothesis and the need to minimize these errors by carefully designing studies and determining appropriate sample sizes. The post also covers acceptable risk levels (α = 0.05 for Type 1, β = 0.2 for Type 2), the significance of study results, and the power of a study.

The blog post explains the concepts of p-value and confidence interval in clinical research. It discusses hypothesis testing (p-value) and quantification of effect (confidence interval) as methods to determine statistical significance. P-value assesses the probability that observed effects are due to chance, while confidence intervals estimate the range within which the true population parameter lies. Confidence intervals provide more informative results, but both methods have their place depending on the context of the research.

The blog post offers a detailed guide on conducting systematic reviews, emphasizing their structured and comprehensive nature. It outlines key steps: formulating a clear research question, systematic and exhaustive literature search, and unbiased study selection involving multiple reviewers. The post differentiates between qualitative and quantitative synthesis and highlights the utility of systematic reviews in providing high-level evidence. Essential stages include evaluating the risk of bias, data extraction, and synthesis, followed by manuscript writing and revision. The post also discusses challenges and the importance of meticulous planning and teamwork.