Importance Large language models (LLMs) may facilitate the labor-intensive process of systematic reviews. However, the exact methods and reliability remain uncertain. Objective To explore the feasibility and reliability of using LLMs to assess risk of bias (ROB) in randomized clinical trials (RCTs). Design, Setting, and Participants A survey study was conducted between August 10, 2023, and October 30, 2023. Thirty RCTs were selected from published systematic reviews. Main Outcomes and Measures A structured prompt was developed to guide ChatGPT (LLM 1) and Claude (LLM 2) in assessing the ROB in these RCTs using a modified version of the Cochrane ROB tool developed by the CLARITY group at McMaster University. Each RCT was assessed twice by both models, and the results were documented. The results were compared with an assessment by 3 experts, which was considered a criterion standard. Correct assessment rates, sensitivity, specificity, and F1 scores were calculated to reflect accuracy, both overall and for each domain of the Cochrane ROB tool; consistent assessment rates and Cohen κ were calculated to gauge consistency; and assessment time was calculated to measure efficiency. Performance between the 2 models was compared using risk differences. Results Both models demonstrated high correct assessment rates. LLM 1 reached a mean correct assessment rate of 84.5% (95% CI, 81.5%-87.3%), and LLM 2 reached a significantly higher rate of 89.5% (95% CI, 87.0%-91.8%). The risk difference between the 2 models was 0.05 (95% CI, 0.01-0.09). In most domains, domain-specific correct rates were around 80% to 90%; however, sensitivity below 0.80 was observed in domains 1 (random sequence generation), 2 (allocation concealment), and 6 (other concerns). Domains 4 (missing outcome data), 5 (selective outcome reporting), and 6 had F1 scores below 0.50. The consistent rates between the 2 assessments were 84.0% for LLM 1 and 87.3% for LLM 2. LLM 1’s κ exceeded 0.80 in 7 and LLM 2’s in 8 domains. The mean (SD) time needed for assessment was 77 (16) seconds for LLM 1 and 53 (12) seconds for LLM 2. Conclusions In this survey study of applying LLMs for ROB assessment, LLM 1 and LLM 2 demonstrated substantial accuracy and consistency in evaluating RCTs, suggesting their potential as supportive tools in systematic review processes.
While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC > 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint.
Mitochondrial genome is a powerful molecule marker to explore phylogenetic relationships and reveal molecular evolution in ichthyological studies. Gerres species play significant roles in marine fishery, but its evolution has received little attention. To date, only two Gerres mitochondrial genomes were reported. In the present study, three mitogenomes of Gerres (Gerres filamentosus, Gerres erythrourus, and Gerres decacanthus) were systemically investigated. The lengths of the mitogenome sequences were 16,673, 16,728, and 16,871 bp for G. filamentosus, G. erythrourus, and G. decacanthus, respectively. Most protein-coding genes (PCGs) were initiated with the typical ATG codon and terminated with the TAA codon, and the incomplete termination codon T/TA could be detected in the three species. The majority of AT-skew and GC-skew values of the 13 PCGs among the three species were negative, and the amplitude of the GC-skew was larger than the AT-skew. The genetic distance and Ka/Ks ratio analyses indicated 13 PCGs were suffering purifying selection and the selection pressures were different from certain deep-sea fishes, were which most likely due to the difference in their living environment. The phylogenetic tree was constructed by molecular method (Bayesian Inference (BI) and maximum Likelihood (ML)), providing further supplement to the scientific classification of fish. Three Gerres species were differentiated in late Cretaceous and early Paleogene, and their evolution might link with the geological events that could change their survival environment.
Background: COVID-19 is not only associated with substantial acute liver and kidney injuries, but is also associated with an increased risk of post-acute sequelae involving the kidney and liver system. We aim to investigate whether COVID-19 infection exposure will increase the long-term risk of kidney or liver disease, and what are the magnitude of the associations and the certainty of risks.Methods: We searched PubMed, Embase, Web of Science, ClinicalTrials.gov, and the Living Overview of the Evidence (L-OVE) COVID-19 Repository, for cohort studies of the association between COVID-19 infection and kidney and liver outcomes. Random effects meta-analyses were performed to combine the results of the included studies. we assessed the certainty of the risks using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach.Findings: Fifteen cohort studies with more than 32 million participants were eligible for the systematic review. Low-certainty evidence suggested that COVID-19 infection was associated with a 35% greater risk of kidney diseases (10 more per 1000 persons) and 54% greater risk of liver disease (3 more per 1000 persons), which include 3 more acute kidney injury per 1000 persons, 8 more chronic kidney disease per 1000 persons, and 3 more liver test abnormality per 1000 persons. Subgroup analyses showed that the differences between different type of kidney and liver diseases were not distinct with moderate credibility.Interpretation: This systematic review and meta-analysis of cohort studies supported the association between COVID-19 infection and the risks of incident kidney and liver outcomes. The absolute magnitude of COVID-19 effect on kidney and liver outcomes was, however, relatively small, and the overall certainty of evidence was low or very low.Funding: None.Declaration of Interest: All other authors declare no competing interests.
Motor load accounts for more than 50% of the total electric power load in China. Identifying the load of induction motors non-intrusively is of great importance for the design of energy-saving schemes and formulation of demand-side response strategies in industrial enterprises. Based on the transient mechanism of the induction motor, the present work first defines some motor load start-up transient feature parameters with clear physical meanings and proposes a set of non-intrusive motor load identification methods applicable to industrial settings. In addition, a case study that applied the proposed method to the industrial setting was performed to verify its effectiveness. The results showed that the proposed method can overcome the problem of misidentification caused by the fact that the start-up transient process is affected by its mechanical load characteristics and hence can identify motors with similar running power and has good anti-interference capacity despite power quality disturbances.
Purpose: To investigate the clinical efficacy of different neo-adjuvant chemotherapy (NACT) regimens in the treatment of advanced oral squamous cell carcinoma (OSCC), and their influence on immune function of the patients.Methods: Advanced OSCC patients (n = 94) who received NACT served as subjects in this study. They were assigned to 2 different treatment groups. Forty patients received docetaxel and fluorouracil regimen (DF group), while 54 patients received taxotere, cisplatin and fluorouracil regimen (TPF group). Surgery was performed after NACT. Changes in clinical efficacy and immune function were monitored in both groups. The clinical baseline data of patients were assessed prior to the treatments. Independent indicators of prognosis were determined using Cox regression analysis (CRA).Results: Clinical treatment efficacy was higher in TPF group than in DF group (p < 0.05). Objective remission rate (ORR) in DF group was lower than that in TPF group (p < 0.05). After chemotherapy, both groups had increased levels of CD4+ and CD4+/CD8+, and reduced level of CD8+, when compared with pre-chemotherapy values, with higher levels of CD4+ and CD4+/CD8+ ratio, and lower level of CD8+ in TPF group than in DF group (p < 0.05). Multivariate CRA revealed that the independent factors for prognosis of oral carcinoma (OC) were tumor node metastasis (TNM) stage and lymph node metastasis.Conclusion: These results indicate that TFP regimen improves clinical efficacy and immune function in patients with advanced OSCC.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
