Combining interscalene brachial plexus block with intravenous-inhalation combined anesthesia for upper extremity fractures surgery: A randomized controlled trial

AbstractBackground: A parallel-group randomized controlled trial (RCT) was conducted to evaluate the effect of combining the interscalene brachial plexus block (IBPB) with Intravenous–inhalation combined anesthesia to isolated Intravenous–inhalation anesthesia in the upper extremity fractures surgery of elderly patients. Methods: One hundred elderly patients who underwent upper extremity surgery were randomly assigned to received isolated Intravenous–inhalation combined anesthesia (group CI, n = 50) and IBPB associated with Intravenous–inhalation combined anesthesia (group NB, n = 50). Associated side effects, recovery time after operation, as well as the dose of intraoperative vasoactive agents and auxiliary drugs were noted. Results: The two groups were not significantly different in gender (P = 0.539), ages (P = 0.683) and weight (P = 0.212). Five patients (10%) in the group NB and 17 patients (34%) in the group CI suffered from preoperative hypotension (P = 0.004). Besides, lower incidence of other adverse effects such as mental stress, incision pain and hypertension were also found in the group NB; however, the differences were not statistically significant (P > 0.05). The consumption of general anesthetics in the group NB was significantly less than that of the group CI (propofol, P = 0.004; lsoflurane, P < 0.001), and the recovery time of the group NB was significantly shorter than that of the group CI (P = 0.020). Conclusion: Combining IBPB with Intravenous–inhalation combined anesthesia in elderly patients hold a greater potential for upper extremity fractures surgery due to its improved clinical effectiveness and fewer side effects

Mediastinal Lymph Node Metastases in Thyroid Cancer: Characteristics, Predictive Factors, and Prognosis

Background. Mediastinal lymph node metastases (MLNM) have not been extensively studied. The aim of this study is to investigate the characteristics, predictive factors, and prognosis of MLNM in thyroid cancer. Methods. This is a retrospective study based on the thyroid cancer patients with MLNM at our institution from 2008 to 2015. Results. In total, 73 thyroid cancer patients with positive MLNM were included in this study. It contained sixty patients (82.2%) with papillary thyroid carcinoma (PTC), twelve (16.4%) with medullary thyroid carcinoma, and one (1.4%) with anaplastic thyroid carcinoma. Forty-eight patients had the surgery as initial treatment. Fifty-three (72.6%) patients remained disease-free, and fifteen (20.5%) developed a regional recurrence. Distant metastases occurred in four (5.5%) patients and five (6.8%) patients died. Five-year overall survival rate and disease-free survival (DFS) rate of the PTC patients for initial treatment are 95.4% and 77.2%, respectively. Extrathyroidal extension and multiple lymph nodes involved were associated with DFS in PTC patients. Conclusions. Initial therapeutic control is very important for the thyroid cancer patients. Extrathyroidal extension and multiple mediastinal lymph nodes involved were the influence factors of prognosis in the thyroid cancer patients with MLNM

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Predisposing factors for predicting the therapeutic response of adenomyosis after uterine artery embolization: serum CA125 levels and accompanying endometriosis

PURPOSE:We aimed to identify predisposing factors that could help predict the therapeutic response of adenomyosis after uterine artery embolization (UAE).METHODS:This was a retrospective, single-center study of patients admitted to the hospital for adenomyosis between 2013 and 2015. Sixty-eight patients with adenomyosis who underwent UAE with tris-acryl gelatin microspheres were divided into two groups based on their therapeutic response (complete or incomplete necrosis of lesions), and pre- and postprocedural pelvic magnetic resonance imaging (MRI) data. Patients were followed up for 12 months after UAE. Improvements in dysmenorrhea and menorrhagia were evaluated based on the symptom relief criteria. Improvement rates in both groups were analyzed and compared. Multivariate logistic regression analysis was used to identify the predisposing factors from retrospectively gathered baseline data that might affect the therapeutic response, including MRI features, clinical symptoms, biochemical index, and accompanying diseases of adenomyosis. Then, a prognostic model was established, and the receiver operating characteristic (ROC) curve of identified factors was drawn to determine their predictive value.RESULTS:Following UAE, 46 patients (67.6%) showed complete necrosis, while 22 patients (32.4%) showed incomplete necrosis. At 12-month follow-up, dysmenorrhea symptom improvement was seen in 94.7% of complete necrosis and 50% of incomplete necrosis group (P < 0.001); menorrhagia symptom improvement was seen in 96.2% of complete necrosis and 57.1% of incomplete necrosis groups (P = 0.004). Multivariate logistic regression analysis determined serum cancer antigen 125 (CA125) levels (odds ratio [OR], 1.006; 95% confidence interval [CI], 1.002–1.010; P = 0.005) and accompanying endometriosis (OR, 6.869; 95% CI, 1.881–25.016; P = 0.004) as predisposing factors. The areas under the ROC curve of CA125, endometriosis, and these two indicators combined were 0.785, 0.708, and 0.845, which corresponded to sensitivities of 95.5%, 66.7%, and 68.2% and specificities of 52.2%, 80.0%, and 87.0% at optimal cutoff values, respectively.CONCLUSION:Symptom relief of dysmenorrhea and menorrhagia for patients with complete necrosis was significantly better than that for patients with incomplete necrosis. Serum CA125 levels and accompanying endometriosis can effectively distinguish complete necrosis from incomplete necrosis

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL. However, it faces challenges with strict SQL syntax requirements. Existing work prompts the LLMs with a list of demonstration examples (i.e. question-SQL pairs) to generate SQL, but the fixed prompts can hardly handle the scenario where the semantic gap between the retrieved demonstration and the input question is large. In this paper, we propose a retrieval-augmented prompting method for a LLM-based Text-to-SQL framework, involving sample-aware prompting and a dynamic revision chain. Our approach incorporates sample-aware demonstrations, which include the composition of SQL operators and fine-grained information related to the given question. To retrieve questions sharing similar intents with input questions, we propose two strategies for assisting retrieval. Firstly, we leverage LLMs to simplify the original questions, unifying the syntax and thereby clarifying the users' intentions. To generate executable and accurate SQLs without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback from the previously generated SQL. Experimental results on three Text-to-SQL benchmarks demonstrate the superiority of our method over strong baseline models