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ObjectivesTo synthesise current evidence from systematic reviews (SRs) regarding the efficacy and safety of non-pharmacological interventions to prevent and treat pain in newborn infants.DesignOverview of SRs.Data sourcesWe searched PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Chinese National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Wanfang Database, Chinese Science and Technology Periodical Database (VIP) and Google Scholar to identify all relevant SRs published in the last 5 years.Eligibility criteria for selecting studiesWe included SRs that evaluated the efficacy and safety of non-pharmacological interventions for neonatal pain.Data extraction and synthesisTwo reviewers independently extracted the data, assessed the methodological quality using a Measurement Tool to Assess Systematic Reviews (AMSTAR) 2 and graded the evidence quality with the Grading of Recommendations Assessment, Development and Evaluation (GRADE).ResultsA total of 29 SRs were included in this overview, of which 28 focused on procedural pain and only 1 focused on postoperative pain. Based on AMSTAR 2, seven reviews were found to be of ‘high quality‘, eight of ‘moderate quality’, five of ‘low quality’ and nine of ‘critically low quality’. The GRADE results suggested that facilitated tucking, kangaroo care, sweet solutions, familiar odour or combined non-pharmacological interventions, such as a combination of sucrose and non-nutritive sucking, were effective and safe in reducing pain from medical procedures in neonates. However, sucrose alone was less effective than local anaesthesia or a combination of the two during circumcision.ConclusionsFacilitated tucking, small volumes of sweet solutions, kangaroo care and familiar odour were recommended. Scientific implementation strategies should be developed to promote the clinical use of these effective non-pharmacological interventions. Meanwhile, further rigorous trials and SRs are needed to identify the best non-pharmacological approaches for pain from common surgery and illnesses in neonates.PROSPERO registration numberCRD42021292583.

Aim: To identify the safety, immunogenicity, and protective efficacy of COVID-19 vaccines in children and adolescents. Methods: We conducted a systematic review of published studies and ongoing clinical studies related to the safety, immunogenicity, and efficacy of COVID-19 vaccine in children or adolescents (aged < 18 years). Databases including PubMed, Web of Science, WHO COVID-19 database, and China National Knowledge Infrastructure (CNKI) were searched on 23 July 2021. International Clinical Trials Registry Platform (ICTRP) was also searched to identify ongoing studies. Results: Eight published studies with a total of 2852 children and adolescents and 28 ongoing clinical studies were included. Of the eight published studies, two were RCTs, two case series, and four case reports. The investigated COVID-19 vaccines had good safety profiles in children and adolescents. Injection site pain, fatigue, headache, and chest pain were the most common adverse events. A limited number of cases of myocarditis and pericarditis were reported. The RCTs showed that the immune response to BNT162b2 in adolescents aged 12–15 years was non-inferior to that in young people aged 16–25 years, while with 3 μg CoronaVac injection the immune response was stronger than with 1.5 μg. The efficacy of BNT162b2 was 100% (95% CI: 75.3 to 100), based on one RCT. Of the 28 ongoing clinical studies, twenty-three were interventional studies. The interventional studies were being conducted in fifteen countries, among them, China (10, 43.5%) and United States(9, 39.1%) had the highest number of ongoing trials. BNT162b2 was the most commonly studied vaccine in the ongoing trials. Conclusion: Two COVID-19 vaccines have potential protective effects in children and adolescents, but awareness is needed to monitor possible adverse effects after injection. Clinical studies of the COVID-19 vaccination in children and adolescents with longer follow-up time, larger sample size, and a greater variety of vaccines are still urgently needed.

Background:Living evidence (LE) synthesis refers to the method of continuously updating systematic evidence reviews to incorporate new evidence. It has emerged to address the limitations of the traditional systematic review process, particularly the absence of or delays in publication updates. The emergence of COVID-19 accelerated the progress in the field of LE synthesis, and currently, the applications of artificial intelligence (AI) in LE synthesis are expanding rapidly. However, in which phases of LE synthesis should AI be used remains an unanswered question.Objective:This study aims to (1) document the phases of LE synthesis where AI is used and (2) investigate whether AI improves the efficiency, accuracy, or utility of LE synthesis.Methods:We searched Web of Science, PubMed, the Cochrane Library, Epistemonikos, the Campbell Library, IEEE Xplore, medRxiv, COVID-19 Evidence Network to support Decision-making, and McMaster Health Forum. We used Covidence to facilitate the monthly screening and extraction processes to maintain the LE synthesis process. Studies that used or developed AI or semiautomated tools in the phases of LE synthesis were included.Results:A total of 24 studies were included, including 17 on LE syntheses, with 4 involving tool development, and 7 on living meta-analyses, with 3 involving tool development. First, a total of 34 AI or semiautomated tools were involved, comprising 12 AI tools and 22 semiautomated tools. The most frequently used AI or semiautomated tools were machine learning classifiers (n=5) and the Living Interactive Evidence synthesis platform (n=3). Second, 20 AI or semiautomated tools were used for the data extraction or collection and risk of bias assessment phase, and only 1 AI tool was used for the publication update phase. Third, 3 studies demonstrated the improvement in efficiency achieved based on time, workload, and conflict rate metrics. Nine studies applied AI or semiautomated tools in LE synthesis, obtaining a mean recall rate of 96.24%, and 6 studies achieved a mean F1-score of 92.17%. Additionally, 8 studies reported precision values ranging from 0.2% to 100%.Conclusions:AI and semiautomated tools primarily facilitate data extraction or collection and risk of bias assessment. The use of AI or semiautomated tools in LE synthesis improves efficiency, leading to high accuracy, recall, and F1-scores, while precision varies across tools.Trial Registration:OSF Registries 87tp4; https://osf.io/4fvdq/overview

Chatbots based on large language models (LLMs) have shown promise in evaluating the consistency of research. Previously, researchers used LLM to assess if randomized controlled trial (RCT) abstracts adhered to the CONSORT-Abstract guidelines. However, the consistency of artificial intelligence (AI) interventional RCTs aligning with the CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) standards by LLMs remains unclear. The aim of this study is to identify the consistency of RCTs on AI interventions with CONSORT-AI using chatbots based on LLMs. This cross-sectional study employed 6 LLM models to assess the consistency of RCTs on AI interventions. The sample selection is based on articles published in JAMA Network Open, which included a total of 41 RCTs. All queries were submitted to LLMs through an application programming interface with a temperature setting of 0 to ensure deterministic responses. One researcher posed the questions to each model, while another independently verified the responses for validity before recording the results. The Overall Consistency Score (OCS), recall, inter-rater reliability, and consistency of contents were analyzed. We found gpt-4-0125-preview has the best average OCS on the basis of the results obtained by JAMA Network Open authors and by us (86.5%, 95% CI 82.5%-90.5% and 81.6%, 95% CI 77.6%-85.6%, respectively), followed by gpt-4-1106-preview (80.3%, 95% CI 76.3%-84.3% and 78.0%, 95% CI 74.0%-82.0%, respectively). The model with the worst average OCS is gpt-3.5-turbo-0125 on the basis of the results obtained by JAMA Network Open authors and by us (61.9%, 95% CI 57.9%-65.9% and 63.0%, 95% CI 59.0%-67.0%, respectively). Among the 11 unique items of CONSORT-AI, Item 2 (\"State the inclusion and exclusion criteria at the level of the input data\") received the poorest overall evaluation across the 6 models, with an average OCS of 48.8%. For other items, those with an average OCS greater than 80% across the 6 models included Items 1, 5, 8, and 9. GPT-4 variants demonstrate strong performance in assessing the consistency of RCTs with CONSORT-AI. Nonetheless, refining the prompts could enhance the precision and consistency of the outcomes. While AI tools like GPT-4 variants are valuable, they are not yet fully autonomous in addressing complex and nuanced tasks such as adherence to CONSORT-AI standards. Therefore, integrating AI with higher levels of human supervision and expertise will be crucial to ensuring more reliable and efficient evaluations, ultimately advancing the quality of medical research.

ObjectiveTo examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors.DesignThe prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis.SettingA multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020.ResultsWe retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians.ConclusionOff-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

Objective: The aims of this systematic review and meta-analysis were to summarize the current existing evidence on the outcome of critically ill patients with COVID-19 as well as to evaluate the effectiveness of clinical interventions. Data Sources: We searched MEDLINE, the Cochrane library, Web of Science, the China Biology Medicine disc, China National Knowledge Infrastructure, and Wanfang Data from their inception to May 15, 2021. The search strings consisted of various search terms related to the concepts of mortality of critically ill patients and clinical interventions. Study Selection: After eliminating duplicates, two reviewers independently screened all titles and abstracts first, and then the full texts of potentially relevant articles were reviewed to identify cohort studies and case series that focus on the mortality of critically ill patients and clinical interventions. Main Outcomes and Measures: The primary outcome was the mortality of critically ill patients with COVID-19. The secondary outcomes included all sorts of supportive care. Results: There were 27 cohort studies and six case series involving 42,219 participants that met our inclusion criteria. All-cause mortality in the intensive care unit (ICU) was 35% and mortality in hospital was 32% in critically ill patients with COVID-19 for the year 2020, with very high between-study heterogeneity ( I 2 = 97%; p < 0.01). In a subgroup analysis, the mortality during ICU hospitalization in China was 39%, in Asia—except for China—it was 48%, in Europe it was 34%, in America it was 15%, and in the Middle East it was 39%. Non-surviving patients who had an older age [−8.10, 95% CI (−9.31 to −6.90)], a higher APACHE II score [−4.90, 95% CI (−6.54 to −3.27)], a higher SOFA score [−2.27, 95% CI (−2.95 to −1.59)], and a lower PaO 2 /FiO 2 ratio [34.77, 95% CI (14.68 to 54.85)] than those who survived. Among clinical interventions, invasive mechanical ventilation [risk ratio (RR) 0.49, 95% CI (0.39–0.61)], kidney replacement therapy [RR 0.34, 95% CI (0.26–0.43)], and vasopressor [RR 0.54, 95% CI (0.34–0.88)] were used more in surviving patients. Conclusions: Mortality was high in critically ill patients with COVID-19 based on low-quality evidence and regional difference that existed. The early identification of critical characteristics and the use of support care help to indicate the outcome of critically ill patients.

ObjectivesGenerative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles.Design and settingInternational online Delphi study.ParticipantsInternational experts in medicine and artificial intelligence.Main outcome measuresThe primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research).ResultsThe development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool’s role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions.ConclusionGAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.

Background: Acute infectious diseases constitute the most prevalent public health emergency (PHE) in China. Chinese herbal medicine (CHM) has long been used in the treatment of acute infections, but the overall evidence of its benefit and harm has not been comprehensively and systematically evaluated. Methods: We searched CBM, CNKI, Wanfang, PubMed, Cochrane Library, embase and preprint platforms to retrieve systematic reviews (SRs) on CHM for acute infectious. Participants with COVID-19, SARS, H1N1, tuberculosis, bacillary dysentery, mumps, herpangina, hand-foot-and-mouth disease (HFMD), and other acute infectious diseases were included. Interventional group consisting of patients treated with CHM combined with Western medicine or CHM alone. The AMSTAR 2 tool was used to assess the methodological quality of the retrieved studies. Information on interventions, control measures and outcomes of the included studies was extracted, and meta-analyses were qualitatively synthesized. Results: A total of 51 SRs and meta-analyses were eligible for this overview, including 19 for COVID-19, 11 for hand-foot-and-mouth disease, 8 for severe acute respiratory syndrome (SARS), 4 for tuberculosis, 3 for mumps, 2 for bacillary dysentery, 2 for H1N1 influenza and 2 for herpangina. Six systematic reviews were of high quality, all of which were on the use of CHM for COVID-19; 24 were of moderate quality; 10 were of low quality; and 11 were of very low quality. CHM appeared to have potential benefits in improving clinical symptoms and signs for most infections with an acceptable safety profile, and the clinical evidence of the benefits of CHM for acute respiratory infections such as COVID-19, SARS and H1N1 seems more sufficient than that for other acute infections. Conclusion: Overall, CHM, both decoction and Chinese patent medicine, used alone or in combination with conventional medicine may offer potential benefits to relieving symptoms of people with acute respiratory infections. Full reporting of disease typing, staging, and severity, and intervention details is further required for a better evidence translation to the responses for PHE. Future CHM research should focus mainly on the specific aspects of respiratory infections such as its single use for mild infections, and the adjunct administration for sever infections, and individual CHM prescriptions for well-selected outcomes should be prioritized.

Aims. To investigate the effect of preoperative HbA1c levels on the postoperative outcomes of coronary artery disease surgery in diabetic and nondiabetic patients. Methods and Results. The MEDLINE (via PubMed), Cochrane Library, Web of Science, Embase, Wanfang Data, China National Knowledge Infrastructure (CNKI), and Chinese Biology Medicine (CBM) databases were used to search the effects of different preoperative HbA1c levels on the postoperative outcomes of coronary artery disease surgical treatment in diabetic and nondiabetic patients from inception to December 2018. Two review authors worked in an independent and duplicate manner to select eligible studies, extract data, and assess the risk of bias of the included studies. We used a meta-analysis to synthesize data and analyze subgroups, sensitivity, and publication bias as well as the GRADE methodology if appropriate. The literature search retrieved 886 records initially, and 23 cohort studies were included in the meta-analysis. In this meta-analysis, we found that there was a reduced incidence of surgical site infections (OR=2.94, 95% CI 2.18-3.98), renal failure events (OR=1.63, 95% CI 1.13-2.33), and myocardial infarction events (OR=1.69, 95% CI 1.16-2.47), as well as a shortened hospital stay (MD=1.08, 95% CI 0.46-1.71), in diabetic patients after coronary artery disease surgical treatment with lower preoperative HbA1c levels. For nondiabetic patients, a higher preoperative HbA1c level resulted in an increase in the incidence of mortality (OR=2.23, 95% CI 1.01-4.90) and renal failure (OR=2.33, 95% CI 1.32-4.12). No significant difference was found between higher and lower preoperative HbA1c levels in the incidence of mortality (OR=1.06, 95% CI 0.88-1.26), stroke (OR=1.49, 95% CI 0.94-2.37), or atrial fibrillation (OR=0.94, 95% CI 0.67-1.33); the length of ICU stay (MD=0.20, 95% CI -0.14-0.55); or sepsis incidence (OR=2.49, 95% CI 0.99-6.25) for diabetic patients or for myocardial infarction events (OR=1.32, 95% CI 0.27-6.31) or atrial fibrillation events (OR=0.99, 95% CI 0.74-1.33) for nondiabetic patients. The certainty of evidence was judged to be moderate or low. Conclusion. This meta-analysis showed that higher preoperative HbA1c levels may potentially increase the risk of surgical site infections, renal failure, and myocardial infarction and reduce the length of hospital stay in diabetic subjects after coronary artery disease surgical treatment and increase the risk of mortality and renal failure in nondiabetic patients. However, there was great inconsistency in defining higher preoperative HbA1c levels in the studies included; we still need high-quality RCTs with a sufficiently large sample size to further investigate this issue in the future. This trial is registered with CRD42019121531.