Paediatric safety assessment of BNT162b2 vaccination in a multistate hospital-based electronic health record system in the USA: a retrospective analysis

Age- and sex-stratified risks of myocarditis and pericarditis attributable to COVID-19 vaccination: a systematic review and meta-analysis

Although COVID-19 vaccines are generally very safe, the risks of myocarditis and pericarditis after receiving an messenger RNA (mRNA) vaccine have been established, with the highest risk in young men. Most systematic reviews and meta-analyses of the risk of myocarditis or pericarditis have included passive surveillance data, which is subject to reporting errors. Accurate measures of age-, sex-, and vaccine dose- and type-specific risks are crucial for assessment of the benefits and risks of the vaccination. A systematic review and meta-analysis of the risks of myocarditis and pericarditis attributable COVID-19 vaccines were conducted, stratified by age groups, sex, vaccine type, and vaccine dose. Five electronic databases and gray literature sources were searched on November 21, 2023. Article about studies that compared a COVID-19-vaccinated group with an unvaccinated group or time period (eg, self-controlled) were included. Passive surveillance data were excluded. Meta-analyses were conducted using random-effects models. A total of 4030 records were identified; ultimately, 17 articles were included in this review. Compared with unvaccinated groups or unvaccinated time periods, the highest attributable risk of myocarditis or pericarditis was observed after the second dose in boys aged 12-17 years (10.18 per 100 000 doses [95% CI, 0.50-19.87]) of the BNT162b2 vaccine and in young men aged 18-24 years (attributable risk, 20.02 per 100 000 doses [95% CI, 10.47-29.57]) for the mRNA-1273 vaccine. The stratified results based on active surveillance data provide the most accurate available estimates of the risks of myocarditis and pericarditis attributable to specific COVID-19 vaccinations for specific populations. Trial registration: International Prospective Register of Systematic Reviews (PROSPERO) Identifier: CRD42023443343.

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care.