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IntroductionAs the robotic approach in hepatectomy gains prominence, the need to establish a robotic-specific difficulty scoring system (DSS) is evident. The Tampa Difficulty Score was conceived to bridge this gap, offering a novel and dedicated robotic DSS aimed at improving preoperative surgical planning and predicting potential clinical challenges in robotic hepatectomies. In this study, we internally validated the recently published Tampa DSS by applying the scoring system to our most recent cohort of patients.MethodsThe Tampa Difficulty Score was applied to 170 recent patients who underwent robotic hepatectomy in our center. Patients were classified into: Group 1 (score 1–8, n = 23), Group 2 (score 9–24, n = 120), Group 3 (score 25–32, n = 20), and Group 4 (score 33–49, n = 7). Key variables for each of the groups were analyzed and compared. Statistical significance was accepted at p ≤ 0.05.ResultsNotable correlations were found between the Tampa Difficulty Score and key clinical parameters such as operative duration (p < 0.0001), estimated blood loss (p < 0.0001), and percentage of major resection (p = 0.00007), affirming the score’s predictive capacity for operative technical complexity. The Tampa Difficulty Score also correlated with major complications (Clavien–Dindo ≥ III) (p < 0.0001), length of stay (p = 0.011), and 30-day readmission (p = 0.046) after robotic hepatectomy.ConclusionsThe Tampa Difficulty Score, through the internal validation process, has confirmed its effectiveness in predicting intra- and postoperative outcomes in patients undergoing robotic hepatectomy. The predictive capacity of this system is useful in preoperative surgical planning and risk categorization. External validation is necessary to further explore the accuracy of this robotic DSS.

Artificial intelligence (AI) plays an increasingly pivotal role in advancing sustainable economic development. While existing literature predominantly examines the environmental impact of AI technologies from national or sectoral perspectives, this study provides a micro‐level analysis of its effects on energy conservation and emission reduction (ECER) performance, utilizing a dataset of Chinese listed firms. We employ a large language model (LLM)‐based intelligent scoring system to capture firms' ECER performance from publicly available environmental disclosures, and construct two‐pronged measures of AI technological capabilities encompassing both innovation and adoption dimensions. The empirical analysis demonstrates that AI technologies significantly enhance ECER performance among Chinese listed firms, with results remaining robust to various alternative specifications and robustness tests. Mechanism analysis reveals that AI facilitates environmental improvements through the enhancement of productive efficiency and the promotion of green innovation. Heterogeneity analysis further indicates that AI‐driven environmental effects are more pronounced among state‐owned enterprises, mature‐stage firms, firms in polluting industries, sectors with lower competitive intensity, labor‐intensive and capital‐intensive industries, and firms located in cities with stringent environmental regulations. These findings offer novel firm‐level empirical evidence on AI's environmental implications, contributing to a more comprehensive understanding of the technology‐environment nexus in emerging economies and laying a theoretical foundation for targeted AI‐related environmental policy interventions.

The aim of this study was validation and comparison of stone scoring systems (S.T.O.N.E, GUY, CROES, S-ReSC) used to predict postoperative stone-free status and complications after percutaneous nephrolithotomy (PCNL) for staghorn stones. A total of 160 patients who had staghorn renal stones and underwent PCNL between January 2012 and August 2015 were included in the current retrospective study. Guy, S.T.O.N.E., S-ReSC (Seoul National University Renal Stone Complexity) and CROES (Clinical Research Office of the Endourological Society) nephrolithometry scores were calculated for each patient, and their potential association with stone-free status, operative and fluoroscopy time, and length of hospital stay (LOS) were evaluated. Postoperative complications were graded according to the modified Clavien classification, and the correlation of scoring systems with postoperative complications was also investigated. The mean CROES, S.T.O.N.E, Guy and S-ReSC scores were 143.5 ± 33.6, 9.7 ± 1.6, 3.5 ± 0.5 and 6.2 ± 2.0 respectively. The overall stone-free rate was 59%. All scoring systems were significantly correlated with stone-free status in univariate analysis. However, Guy and S-ReSC scores were the only significant independent predictor in multivariate analysis. And all four nomograms failed to predict complication rates. Current study demonstrated that Guy and S-ReSC scoring systems could effectively predict postoperative stone-free status for staghorn stones. However all four scoring systems failed to predict complication rates.

The relationship between fetal hemoglobin (HbF) levels and disease prognosis in patients with myelodysplastic syndrome (MDS) is unclear. This study aimed to clarify the relationship between HbF level and the prognosis of MDS. To this end, data from 217 patients diagnosed with MDS between April 2006 and August 2020 at Ebina General Hospital were analyzed retrospectively. The primary endpoint was leukemia-free survival (LFS) for 5 years after diagnosis. HbF levels were significantly higher in patients with MDS than in control patients without MDS ( n  = 155), with a cut-off value of 0.4%. Higher-risk patients had a similar prognosis regardless of HbF level, but lower-risk patients had longer LFS at intermediate HbF levels. Although prognosis based on pre-treatment HbF levels did not differ significantly among azacitidine-treated patients, prognosis tended to be better in lower-risk patients with intermediate HbF levels. Multivariate analysis showed that the intermediate HbF category correlated with LFS, independently of MDS lower-risk prognostic scoring system (LR-PSS)-related factors. This study is the first to assess the association between HbF levels and the new World Health Organization 2016 criteria for MDS, demonstrating the significance of HbF levels in the prognosis of MDS.

Purpose Management of quadrilateral plate fractures is technically demanding and requires specific fixation techniques. Infra-pectineal plating is the gold standard method of fixation. However, we recorded a high incidence of medial wall displacement and reoperations. Therefore, the aim of our study was to identify whether supra-pectineal quadrilateral buttress plating provides much more rigid fixation with a better functional and radiological outcome or not. Patients and Methods The authors conducted this prospective, randomized control, single-blinded study at a level 1 single trauma centre. Between March 2022 and June 2023, 34 patients with quadrilateral plate fractures had anterior fixation, either via the anatomical QLP (17 cases) or infra-pectineal plating (17 cases) (Groups A and B, respectively). The radiological and clinical outcomes, as well as residual medial wall displacement, were the primary outcomes. Results The mean follow-up was 14.47 months in group A and 15.24 months in group B. In group A, the mean operative time ( p  = 0.02) was shorter, and the mean blood loss ( p  < 0.001) was significantly lower. However last follow-up showed no statistically significant differences as regards residual medial wall displacement ( p  = 1.0), final radiological ( p  = 0.86), and clinical outcomes ( p  = 1.0). Conclusion Authors concluded that the anatomical QLP made it easier to reduce and fix acetabular fractures with a displaced medial wall. This was done by using multidirectional screws in the posterior column through its infra-pectineal extension and a strong screw purchase aimed at the posterior column through its supra-pectineal part . The two groups were similar in terms of final radiological and clinical outcomes, as well as residual medial wall displacement rates. However, the QLP had less morbidity than the classic infra-pectineal plating (shorter operation time and less blood loss).

Head and neck injuries are a heterogeneous group in terms of both clinical course and prognosis. For years, there have been attempts to create an ideal tool to predict the outcomes and severity of injuries. The aim of this study was evaluation of the use of selected artificial intelligence methods for outcome predictions of head and neck injuries. 6,824 consecutive cases of patients who sustained head and neck injuries, treated in hospitals in the Lublin Province between 2006-2018, whose data was provided by National Institute of Public Health / National Institute of Hygiene, were analyzed retrospectively. Patients were qualified using International Statistical Classification of Diseases and Related Health Problems (10th Revision). The multilayer perceptron (MLP) structure was utilized in numerical studies. Neural network training was achieved with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. In the designed network, the highest classification efficiency was obtained for the group of deaths (80.7%). The average value of correct classifications for all analyzed cases was 66%. The most important variable influencing the prognosis of an injured patient was diagnosis (weight 1.929). Gender and age were variables of less significance with weight 1.08 and 1.073, respectively. Designing a neural network was hindered due to the large amount of cases and linking of a large number of deaths with specific diagnosis (S06). With a predictive value of 80.7% for mortality, ANN can be a promising tool in the future; however, additional variables should be introduced into the algorithm to increase the predictive value of the network. Further studies, including other types of injuries and additional variables, are needed to introduce this method into clinical use.

As network technology has advanced, and as larger and larger quantities of data are being collected, networks are becoming increasingly complex. Various vulnerabilities are being identified in such networks, and related attacks are continuously occurring. To solve these problems and improve the overall quality of network security, a network risk scoring technique using attack graphs and vulnerability information must be used. This technology calculates the degree of risk by collecting information and related vulnerabilities in the nodes and the edges existing in the network-based attack graph, and then determining the degree of risk in a specific network location or the degree of risk occurring when a specific route is passed within the network. However, in most previous research, the risk of the entire route has been calculated and evaluated based on node information, rather than edge information. Since these methods do not include correlations between nodes, it is relatively difficult to evaluate the risk. Therefore, in this paper, we propose a vulnerability Correlation and Attack Graph-based node-edge Scoring System (VCAG-SS) that can accurately measure the risk of a specific route. The proposed method uses the Common Vulnerability Scoring System (CVSS) along with node and edge information. Performing the previously proposed arithmetic evaluation of confidentiality, integrity, and availability (CIA) and analyzing the correlation of vulnerabilities between each node make it possible to calculate the attack priority. In the experiment, the risk scores of nodes and edges and the risk of each attack route were calculated. Moreover, the most threatening attack route was found by comparing the attack route risk. This confirmed that the proposed method calculated the risk of the network attack route and was able to effectively select the network route by providing the network route priority according to the risk score.

Different types of inflammation in the sinuses require different treatments. Ethmoid dominance was proposed as an indicator on computed tomography (CT) images for type 2 chronic rhinosinusitis with nasal polyp (CRSwNP). This study evaluated the predictive value of the ethmoid-to-maxillary (E/M) ratio based on different CT scoring systems in type 2 CRSwNP. Adult patients with bilateral CRSwNP planning to undergo sinus surgery were prospectively recruited. CT images were evaluated using the Lund-Mackay (L-M) and Zinreich scoring systems which involved a more meticulous quantification of opacification on CT images. Tissue eosinophil count (TEC) was determined by histopathological analysis. The expression levels of type 2 cytokines in nasal polyps, including IL-5, IL-13, and eosinophil cationic protein (ECP), were measured using real-time PCR. A total of 174 participants were enrolled. Eighty of these participants exhibited an E/M ratio >1, 49 presented with E/M ratio=1, and 45 showed an E/M ratio <1 based on L-M CT scores. Twenty of the 49 (40.8%) patients with E/M ratio=1 on L-M score turned to E/M ratio >1 after re-evaluation by Zinreich scoring system. The E/M ratio based on the L-M and Zinreich scoring systems both exhibited correlation with the tissue markers of type 2 inflammation, including TEC, interleukin (IL)-5, IL-13, and ECP expression levels, although the Zinreich E/M ratio showed a higher correlation coefficient. The E/M ratio based on the L-M and Zinreich scoring systems correlated with tissue markers of type 2 inflammation. A scale with an E/M ratio of 1 in the L-M system should be further investigated using a more detailed scoring system to determine the presence of an ethmoid-dominant shadow. This could help clinicians better evaluate CT images to determine the severity of type 2 inflammation in patients with CRSwNP and provide optimal therapeutic strategies.

Objective We aimed to identify the clinical, biochemical, and endoscopic features associated with in-hospital mortality after acute upper gastrointestinal bleeding (AUGIB), focusing on cross-validation of the Glasgow-Blatchford score (GBS), full Rockall score (RS), and Cedars-Sinai Medical Center Predictive Index (CSMCPI) scoring systems. Methods Our prospective cross-sectional study included 156 patients with AUGIB. Several statistical approaches were used to assess the predictive accuracy of the scoring systems. Results All three scoring systems were able to accurately predict in-hospital mortality (area under the receiver operating characteristic curve [AUC] > 0.9); however, the multiple logistic model separated the presence of hemodynamic instability (state of shock) and the CSMCPI as the only significant predictive risk factors. In compliance with the overall results, the CSMCPI was consistently found to be superior to the other two systems (highest AUC, highest sensitivity and specificity, highest positive and negative predictive values, highest positive likelihood ratio, lowest negative likelihood ratio, and 1-unit increase in CSMCPI associated with 6.3 times higher odds of mortality), outperforming the GBS and full RS. Conclusions We suggest consideration of the CSMCPI as a readily available and reliable tool for accurately predicting in-hospital mortality after AUGIB, thus providing an essential backbone in clinical decision-making.

Community-acquired pneumonia (CAP) is a significant global health issue, leading to high morbidity and mortality rates. Despite the existence of various severity scoring systems, accurately predicting patient outcomes remains challenging. The CAP-PIRO (Predisposition, Insult, Response, and Organ dysfunction) scoring system offers a comprehensive approach to evaluating CAP severity and prognosis. This study aimed to assess the effectiveness of the CAP-PIRO scoring system in predicting the prognosis and severity of CAP patients, focusing on the development of acute respiratory distress syndrome (ARDS) and 28-day mortality. A total of 875 CAP patients were prospectively enrolled from the emergency department of Beijing Chao-yang Hospital between November 2017 and December 2023. Clinical data, including patient demographics, medical history, vital signs, and laboratory findings, were collected within 6 hours of admission. CAP-PIRO, CURB-65, and PSI scores were calculated. Patients were stratified based on ARDS development, 28-day mortality, and PaO2/FiO2 categories (≤100 mmHg, 100-200 mmHg, 200-300 mmHg). Significant differences were observed in PCT, blood lactate (Lac), CURB-65, PSI, and CAP-PIRO scores between patients with and without ARDS, as well as between survivors and non-survivors at 28 days (P<0.05). CAP-PIRO and Lac were identified as independent predictors for ARDS development and 28-day mortality. The area under the ROC curve (AUC) for CAP-PIRO was higher than that for CURB-65 and PSI in predicting 28-day mortality. The combination of CAP-PIRO and Lac demonstrated improved predictive accuracy for ARDS. Notably, significant differences in CAP-PIRO scores were observed across different PaO2/FiO2 groups. CAP-PIRO demonstrates strong predictive ability for adverse outcomes and, when combined with lactate, shows enhanced predictive power. These findings underscore the value of CAP-PIRO for clinical risk stratification in CAP patients.