Reference Tool Research Articles

Public Awareness and Acceptability of PGT-M in Cancer Predisposition Syndromes.

Cancer Predisposition Syndromes (CPSs), also known as Hereditary Cancer Syndromes (HCSs), represent a group of genetic disorders associated with an increased lifetime risk of developing cancer. In this article, we provide an overview of the reproductive options for patients diagnosed with CPS, focusing on the emerging role of Preimplantation Genetic Testing for Monogenic disorders (PGT-M). Specifically, we conducted a literature review about the awareness and acceptability of its application to CPSs. Based on the available data, the awareness of the applicability of PGT-M for CPSs appears to be limited among both patients and physicians, and a heterogeneous set of factors seems to influence the acceptability of the procedure. Our findings highlight the need for increasing education about the use of PGT-M for CPSs. In this context, guidelines developed by professional or institutional bodies would represent a useful reference tool to assist healthcare professionals in providing proper preconception counseling.

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma.

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment. A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index. The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups. This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

Improving lung cancer screening starts with the primary care provider: Enhancing shared decision making for optimal patient outcomes.

306 Background: Despite the critical importance of lung cancer screening (LCS), its uptake remains dismally low, with only 5.8% of eligible individuals undergoing screening. To harness the potential benefits of LCS, there is an urgent need for shared decision making (SDM) between patients and primary care physicians (PCPs), who often serve as the initial point of contact. Recognizing this need, the Centers for Medicare and Medicaid Services endorse LCS through low-dose computed tomography (LDCT) following SDM. This study aims to enhance SDM and increase LCS utilization by providing PCPs with a comprehensive checklist to address key points and better utilize their time. Methods: A concise checklist was created, which included pertinent points to address during an SDM meeting. This checklist was then given to the PCPs. Fifty-eight (58) PCPs affiliated with two hospitals in the Indiana University Health system agreed to implement the LCS-SDM checklist during office visits with eligible patients. All participating PCPs had existing relationships with LCS-eligible patients, received specific instructions for implementing the LCS-SDM checklist, and agreed to allocate a portion of their visits for SDM. In total, 194 patients on Medicare participated in this study and were interviewed: 158 had no documented history of LCS discussions, while 36 had previously been offered and refused LDCT. Following SDM interviews, PCPs completed written surveys to evaluate the impact of the LCS-SDM checklist on their practice and the perceived effectiveness of SDM. The patients were also surveyed to assess their receptiveness to the SDM process. Results: The majority of the 58 PCPs expressed support for utilizing SDM in LCS; however, time constraints emerged as a significant limiting factor. Despite feeling rushed during the SDM sessions, the PCPs acknowledged the value of the checklist as a helpful reference tool. Among the 158 patients who had not previously discussed LCS, 91 out of 158 patients (57.6%) opted to undergo screening. Among the 36 patients who initially declined LDCT, 20 out of 36 patients (55.5%) chose to participate in LCS after revisiting the topic. Impressively, 192 out of 194 patients (98.9%) expressed appreciation for the opportunity to engage in SDM. Conclusions: The results underscore the significant benefits of SDM in LCS, demonstrating its potential to improve uptake and patient outcomes. While time constraints present challenges for PCPs, both patients and physicians generally respond positively to SDM sessions. By implementing comprehensive checklists and facilitating dedicated time for SDM, primary care providers can effectively enhance LCS utilization and empower patients to make informed decisions regarding their lung health.

The development of a social prescribing glossary of terms to facilitate engagement & communication

Abstract Background Social prescribing (SP) offers an alternative and adjunctive form of holistic support for many issues, through engagement in community-based activities. SP has undergone a period of growth and development, accompanied by a diverse and confusing terminology that creates barriers to engagement and communication. Methods We used a mixed methods approach to collate the terminology associated with SP: Consultation with the SP workforce, a group concept mapping study (GCM), and a scoping review of UK peer-reviewed literature and Welsh grey literature. Results We identified a total of 426 terms, many of which described the same specific elements of SP. Scoping review data was charted from 205 documents: 60% of terms occurred solely in peer-reviewed literature, 20% occurred solely in Welsh grey literature, with an overlap of 10% across both sources. Grey literature contained a higher number of terms per article. Data from the scoping review and GCM study indicated a bias for articles and terminology from England and the healthcare sector that does not necessarily reflect the terminology commonly used by the SP workforce. Preferences for some terms were sector-specific, although many of the terms identified were seemingly used interchangeably with little standardisation across and within specific services. Conclusions This research highlights the breadth and diversity of the terminology associated with SP. Results were used to produce an innovative and interactive glossary of terms, in the form of a PDF and a website. Unlike traditional glossaries, it incorporates a description of the terms, shows the connectedness of terms, and identifies alternative terms and preferences for certain terms across sectors. The glossary of terms for SP provides an informative reference tool for the general public, SP workforce and commissioners. It will improve understanding across sectors and professions, and facilitate the standardisation of the language associated with SP. Key messages • The language associated with social prescribing is diverse and confusing, creating barriers to engagement and impairing communication with the public, and across sectors and professions. • The glossary of terms for social prescribing, that resulted from this research, provides an informative reference tool for the general public, social prescribing workforce and commissioners.

A Database Extension for a Safety Evaluation of a Hydrogen Refueling Station with a Barrier Using a CFD Analysis and a Machine Learning Method

A methodology is proposed to extend datasets in a database suitable for use as a reference tool to support an evaluation of damage mitigation by a barrier wall in a hydrogen refueling station (HRS) during a vapor cloud explosion (VCE) accident. This is realized with a computational fluid dynamic (CFD) analysis and machine learning (ML) technology because measured data from hydrogen explosion tests with various installed barrier models usually require considerable amounts of time, a secured space, and precise measurements. A CFD sensitivity calculation was conducted using the radXiFoam v1.0 code and the established analysis methodology with an error range of approximately ±30% while changing the barrier height from that was used in an experiment conducted by the Stanford Research Institute (SRI) to investigate the effect of the barrier height on the reduction in peak overpressures from an explosion site to far fields in an open space. The radXiFoam code was developed based on the open-source CFD software OpenFOAM-v2112 to simulate a VCE accident in a humid air environment at a compressed gaseous or liquefied HRS. We attempted to extend the number of datasets in the VCE database through the use of the ML method on the basis of pressure data predicted by a CFD sensitivity calculation, also uncovering the possibility of utilizing the ML method to extend the VCE database. The data produced by the CFD sensitivity calculation and the ML method will be examined to confirm their validity and applicability to hypothetical VCE accident simulations if the related test data can be produced during experimental research. The database constructed using core data from the experiment and extended data from the CFD analysis and the ML method will be used to increase the credibility of radXiFoam analysis results for VCE accident scenarios at HRSs, ultimately contributing to safety assurances of HRSs in Republic of Korea.

CORPUS USED AS A DATA-DRIVEN LEARNING TOOL IN L2 ACADEMIC WRITING: EVIDENCE FROM TURKISH CONTEXTS

This study investigated the effectiveness of using corpus as a data-driven learning (DDL) tool to enhance the academic writing skills of Turkish EFL learners. The study also explored learners’ views of the potential use of corpus in L2 academic writing. To achieve these objectives, a mixed-method sequential explanatory design was employed, involving freshman student teachers enrolled in the Department of English Language Teaching at a state university in Turkey. The participants completed four argumentative essay writing tasks. Two tasks employed conventional techniques for error correction, while the other two utilized corpus as a reference tool for error correction. The latter two tasks were complemented by corpus training for the participants. The results indicated that using corpus as a DDL tool had a significant impact on the academic writing skills of Turkish EFL learners, with notable improvements observed in both grammar and vocabulary use. Participants also expressed positive feedback on the use of corpus as a DDL tool in enhancing their L2 academic writing.

Effect of immunology biomarkers associated with hip fracture and fracture risk in older adults

Osteoporosis is a skeletal disease that can increase the risk of fractures, leading to adverse health and socioeconomic consequences. However, current clinical methods have limitations in accurately estimating fracture risk, particularly in older adults. Thus, new technologies are necessary to improve the accuracy of fracture risk estimation. In this observational study, we aimed to explore the association between serum cytokines and hip fracture status in older adults, and their associations with fracture risk using the FRAX reference tool. We investigated the use of a proximity extension assay (PEA) with Olink. We compared the characteristics of the population, functional status and detailed body composition (determined using densitometry) between groups. We enrolled 40 participants, including 20 with hip fracture and 20 without fracture, and studied 46 cytokines in their serum. After conducting a score plot and two unpaired t-tests using the Benjamini-Hochberg method, we found that Interleukin 6 (IL-6), Lymphotoxin-alpha (LT-α), Fms-related tyrosine kinase 3 ligand (FLT3LG), Colony stimulating factor 1 (CSF1), and Chemokine (C-C motif) ligand 7 (CCL7) were significantly different between fracture and non-fracture patients (p < 0.05). IL-6 had a moderate correlation with FRAX (R2 = 0.409, p < 0.001), while CSF1 and CCL7 had weak correlations with FRAX. LT-α and FLT3LG exhibited a negative correlation with the risk of fracture. Our results suggest that targeted proteomic tools have the capability to identify differentially regulated proteins and may serve as potential markers for estimating fracture risk. However, longitudinal studies will be necessary to validate these results and determine the temporal patterns of changes in cytokine profiles.

Comparative analysis of chloroplast genomes of two Chinese local citrus varieties and haplotype analysis with other citrus species

Citrus plants, comprising some of the most widely cultivated fruit crops worldwide, have great research value. We assembled and annotated the chloroplast genomes of two local important citrus varieties of China, ‘Tuju’ and ‘Zangju’, and comparatively analyzed their sequences. The full lengths chloroplast genomes of ‘Tuju’ and ‘Zangju’ were 160,121 and 160,694 bp, respectively. Both featured a quadripartite structure with two copies of a large inverted repeat (IR) separated by small (SSC) and large (LSC) single-copy regions. ‘Tuju’ chloroplast genome contained 134 genes. ‘Zangju’ had one less protein-coding gene (rpl22) than ‘Tuju’. We also found 27 and 21 optimal codons, all of which ended with A or U, for chloroplast protein-coding genes of ‘Tuju’ and ‘Zangju’, respectively. There were 32 haplotypes obtained from 43 citrus chloroplast genomes, some different species or cultivars could share one haplotype. Of them, ‘Tuju’, one individual of Citrus limon, one individual of Citrus maxima and Citrus platymamma shared one haplotype, which implied they had the same maternal origin. The phylogenetic trees supported that all haplotypes could be divided five groups, while haplotype network analysis tended to get three groups. Our study reveals the complex lineage relationship of citrus plants at a certain level, and provides a valuable reference tool for future research on citrus plants.

Thermal comfort performance prediction method using sports center layout images in several cold cities based on CNN

Sports centers are core spaces for urban sports and fitness, and reasonable layout of large public buildings can alleviate urban climate issues. But the adverse effects of planning and layout and surrounding urban microclimate on the thermal comfort of outdoor activity space are rarely considered. In this study, an EnergyPlus and Openfoam collaborative computing platform was constructed to calculate the universal thermal climate index of the outdoor space of sports center. By comparing the simulation results of sports centers in three severely cold cities, improving the wind environment of the site's microclimate was found to be key in improving thermal comfort, and the hours of overall site thermal comfort increased mainly in spring and autumn. In this paper, a design method combining numerical simulations and a convolutional neural network was proposed, which can effectively predict the thermal comfort performance of affected sites. The application of this prediction method to the design of the Yingkou Sports Center increased the hours of overall site thermal comfort by 15.01% compared with the original scheme. Synthesizing the results and methods of this study can provide an effective reference and analysis tool for the overall environmental performance of urban areas.

Assessment and treatment of nonsurgical thumb carpometacarpal joint osteoarthritis: A modified Delphi-based consensus paper of the American Society of Hand Therapists

BackgroundWhile the literature is abundant on hand therapy assessment and treatment of nonsurgical thumb carpometacarpal (CMC) osteoarthritis (OA), clarity and uniformity are meager, making it a desirable diagnosis to establish expert consensus. PurposeThis study aimed to ascertain if consensus exists for the assessment and treatment of nonsurgical management of thumb CMC OA in the hand therapy clinical setting. Study DesignThis was a consensus paper via the modified Delphi approach. MethodsA modified Delphi method was used to determine consensus among an expert panel, including hand therapists and hand surgeons, via two online surveys. A consensus paper steering committee (from the American Society of Hand Therapist’s research division) designed the surveys and analyzed responses. Consensus was established as 75% agreement among the expert panel. Demographic information was collected from the expert panel. ResultsThe expert panel included 34 hand therapists and seven hand surgeons. The survey response rates were 93.6% for the first survey and 90.2% for the second survey. Consensus recommendations were classified according to the World Health Organization categorization. These included evaluating the body structures for clinical signs/clinical testing and body functions for pain, range of motion for palmar abduction, radial abduction, opposition, and thumb metacarpal phalangeal flexion/extension, and grip and tripod pinch strength. Further consensus recommendations were for the assessment of function using a region-specific, upper extremity patient-reported outcome measure (activity and participation), environmental factors, outcome expectation, and illness perception within the patient’s unique environmental and social contexts. Treatment recommendations included the use of an orthosis during painful activities, a dynamic stability program (stable C posture, release of tight adductors, and strengthening of stabilizers), patient education, joint protection techniques, adaptive equipment, and functional-based intervention. ConclusionsThe findings describe the consensus of a group of experts and provide a clinical reference tool on the hand therapy assessment and treatment of nonsurgical thumb CMC joint OA.

The Colourant Mapping Project: approaching the evidence of colourant production in the Aegean during the Hellenistic and Roman Period through Kea, Kos and Rhodes

Over the last decades, polychromy research has provided insight into the rich variety of materials that composed the ancient Mediterranean palette, including minerals, rocks, earths, metal oxides, lakes and pyrotechnological products. Even though polychromy is considered an important aspect of ancient Greek and Roman art, the organisation of colourant production and trade remains rather underexplored. This is possibly due to the inherent complexity of pigments and dyes as archaeological materials and the variability of possible sources. Recent methodological advances have demonstrated the potential to trace the provenance of raw materials and to identify colourant production loci. This paper introduces the Colourant Mapping Project (CMP), which we are currently developing as a novel bibliographic and dissemination tool for researching colourant production and trade. The Aegean islands of Kea, Kos and Rhodes are three case studies included in the CMP that provide varied evidence of colourant production through a combined methodology using literary sources, archaeological and geological information for evaluating potential colourant production sites. Kea is mentioned in ancient texts as an excellent source of red earth, controlled by Athenian exportation legislation. The geology of Kea supports this; however, no earth pigment production sites are currently known and archaeometric evidence connecting to local raw materials is lacking. Kos, on the other hand, lacks historical evidence for local colourant production while a recently well-described multicrafting workshop and abundant archaeological finds substantiate colourant production on this island. The geological evidence also supports a local provenance for some of the materials processed on Kos (earth pigments), yet raw materials such as copper (for Egyptian blue) still required importation from elsewhere. Lastly, Rhodes was praised for its lead white and murex purple production by ancient writers and archaeological evidence might verify both, although not as clearly as is the case of Koan colourant production. The geological information for Rhodes also hints at raw material importation as no local lead ore deposits are known. The aim of the CMP is therefore twofold: on the one hand to evaluate the contribution of varied pieces of evidence to colourant provenancing and production, and on the other to situate the evidence of local colourant production within the context of Mediterranean networks of economic and cultural exchange. An ever-increasing and openly accessible bibliographic reference tool such as the CMP enables the investigation of the provenance of raw materials and colourants and can contribute to the reconstruction of ancient exchange networks.

Online Intention Recognition With Incomplete Information Based on a Weighted Contrastive Predictive Coding Model in Wargame.

The incomplete and imperfect essence of the battlefield situation results in a challenge to the efficiency, stability, and reliability of traditional intention recognition methods. For this problem, we propose a deep learning architecture that consists of a contrastive predictive coding (CPC) model, a variable-length long short-term memory network (LSTM) model, and an attention weight allocator for online intention recognition with incomplete information in wargame (W-CPCLSTM). First, based on the typical characteristics of intelligence data, a CPC model is designed to capture more global structures from limited battlefield information. Then, a variable-length LSTM model is employed to classify the learned representations into predefined intention categories. Next, a weighted approach to the training attention of CPC and LSTM is introduced to allow for the stability of the model. Finally, performance evaluation and application analysis of the proposed model for the online intention recognition task were carried out based on four different degrees of detection information and a perfect situation of ideal conditions in a wargame. Besides, we explored the effect of different lengths of intelligence data on recognition performance and gave application examples of the proposed model to a wargame platform. The simulation results demonstrate that our method not only contributes to the growth of recognition stability, but it also improves recognition accuracy by 7%-11%, 3%-7%, 3%-13%, and 3%-7%, the recognition speed by 6-32x, 4-18x, 13-*x, and 1-6x compared with the traditional LSTM, classical FCN, OctConv, and OctFCN models, respectively, which characterizes it as a promising reference tool for command decision-making.

REVIEW: APPLICATION OF THE ATC/DDD METHOD FOR ANTIBIOTIC EVALUATION IN INDONESIA

The global consumption of antibiotics has increased rapidly by 65% in units of daily doses over the last 15 years. Researchers predict that there will be a 200% increase in global antibiotic consumption by 2030, if there is no change in policy implementation. This article aims to provide knowledge regarding the use of the anatomical therapeutic chemical/defined daily doses (ATC/DDD) method to evaluate antibiotics in Indonesia, which is expected to provide additional knowledge for the quantitative evaluation of antibiotics. The literature data sources used were PubMed and Google Scholar online databases, using the Mendeley® tool for manager reference. There were 71 articles that met the criteria and were discussed systematically. Most of the ATC/DDD methods were used to evaluate antibiotics in 90.1% of inpatients and 9.9% of outpatients. In the application of this method, 94.4% of the data collection was carried out retrospectively, with most study designs using a cross-sectional 76%. The selected research period varied from 1 month to 5 years, with 59.1% of the study locations being carried out at the tertiary service level. The ATC/DDD method can also be used in a quasi-experimental design that examines comparisons before and after the intervention. The use of this method as an evaluation of the use of antibiotics in the specified study population resulted in the highest DDD/100 days of ceftriaxone hospitalization in 27 articles. Amoxicillin had the highest DDD/1000 patient-days in of 6/7 articles in the outpatient population...

Influence of Grooves Angle of Textured Ceramic Tools on the Final Roughness and Cutting Forces of Cold Work Tool Steel

Ceramic tools are a feasible solution for machining hard steels due to their excellent performance in high-temperature environments, which improves productivity. However, the high cutting speeds achieved with ceramic tools can generate cutting temperatures so high that they damage the surface roughness of the component. Therefore, this study aims to analyze the influence of different texturing inclinations on the final roughness of the component and the cutting forces during cutting on Al2O3 with TiC for the turning of 1.2990 cold work tool steel. The experimental setup involved testing five different texture grooves inclinations on the rake face of CNGA 120408 inserts, with a recommended cutting speed of 180 m/min, feed of 0.15 mm/rev, and a depth of cut of 0.5 mm. The tests were carried out in dry conditions. Results showed that the textured tools produced lower cutting forces (8%) and better surface roughness (improvement of 16% for Ra and 18% for Rz) than the reference tool without texture. Therefore, texturing inclinations can be used to mitigate the impact of high cutting speeds on the component during ceramic tool machining.

Metal-Containing Diamond-Like Carbon Coatings with Increased Temperature Stability for Wear Protection of Nitrided Hot Forging Dies

In cold forging processes, diamond-like carbon (DLC) coatings have become established as a wear protection for forging dies, featuring high hardness and advantageous friction properties. This counteracts the particular critical abrasive tool wear and increases tool life. A major disadvantage of DLC coatings is their low thermal stability. In this study, the influence of metallic elements (niobium, tungsten and chromium) in the treatment atmosphere of the coating process is investigated with the aim of increasing the temperature resistance of the DLC coating and enabling its application as a wear-protection measure for hot forging dies. Preliminary studies were carried out to investigate the influence of different treatment atmospheres on wear-reducing properties such as high hardness and coating adhesion depending on prior nitriding processes. The most promising metal-doped DLC coating, with 30 % tungsten in the treatment atmosphere, was used in serial forging tests. At a blank temperature of 1,200 °C and a moderate count of 100 forging cycles, wear was reduced by up to 60 % compared to the nitrided reference tool.

Concordance and validity between versions of the ADHD Conners scale for Parents

IntroductionThe Conners Scale for Parents (CRS-P) is one of the reference tools for the diagnostic assessment of Attention Deficit and Hyperactivity Disorders (ADHD). It is commonly used in both research and clinical practice. The latest edition of the Conners Parents Scale Long Version (CRS-P3L) has undergone extensive modifications but has never been compared with the 2nd edition (CRS-P2L). We aim to study the concordance between the last two editions of the CRS-P, their internal consistency, and their validity against the criteria of the ADHD-RS. MethodsThe study population was a cohort of 30 children diagnosed with ADHD participating in a clinical trial. The parents of these children completed both editions of the CRS (P2L and P3L), as well as a DSM-IV ADHD Diagnostic Criteria Rating Scale (ADHD-RS). A linear regression model with the calculation of Lin's concordance coefficient (LCC) was used to study the concordance between the scales. Internal validity was estimated with Cronbach's alpha and inter-criteria validity with Spearman's correlation coefficient. ResultsThe internal consistency found was “correct” to “good” for both editions (Cronbach alpha 0.85 and 0.77), their correlation with the ADHD-RS was medium to low (Spearman's coefficient 0.25 and 0.09). Concordance between the overall score and the sub-scores of the two editions of the same Conners scale (CRS-P2L and CRS-P3L) was fair to medium (LCC 0.29 to 0.69). ConclusionsThe third edition of the long version of the CRS-P showed very poor concordance with the previous edition. The diagnostic profile of the children seems to have evolved with the new edition, which appears to affect the interpretation of the tests.

Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training data.

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower-resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low- or intermediate-resolution ones. Compared with the reference tools HaploStats, HLA-EMMA, and HLA-Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European-independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low-resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost-effectiveness of HLA typing, considering not only well-imputed second field typing(s) but also well-imputed eplets.

Level of Agreement of the Service Level Index (SLI) Tool with a Standard Service Provision Assessment Tool for Measurement of Antenatal Care Service Provision

The generation of health service delivery data in middle- and low-income countries typically relies on health facility surveys and routine health monitoring data. Previous studies on antenatal care (ANC) service delivery have not employed measurement parameters that accurately and cost-effectively encompass various aspects of ANC service availability, content, and organization. This study aimed to assess the agreement level between the Service Level Index (SLI) tool, which measures ANC service provision, and a standard Service Provision Assessment tool. The SLI tool integrates pertinent sub-domains relevant to ANC service provision processes at the health facility level, employing selected key elements from these sub-domains as proxy measures. This approach minimizes the complexities associated with the time and effort required for assessing service delivery comprehensively. The study examined the agreement between the Service Level Index tool and the ANC module of the MEASURE/DHS Service Provision Assessment tool. To accomplish this, the results obtained from the service level index measurement for each health facility were compared with the outcomes derived from the MEASURE/DHS Service Provision Assessment tool. Statistical analysis using the Bland-Altman method was employed to ascertain the significance of differences between measurements obtained from the Service Level Index tool and the Service Provision Assessment tool. The estimated mean difference (d) and standard deviation (sd) were 0.1 and 0.507, respectively. The agreement level between the two tools (-1.19 to 1.46) indicated that, for 95% of observations, the scoring of ANC service provision conducted using the SLI tool deviated between 1.19% lower and 1.46% higher than the scores generated by the reference tool. The results revealed that the Service Level Index tool has the potential to serve as an alternative to the ANC module of the standard Service Provision Assessment tool for evaluating ANC services at the micro-level.

Evaluating the Sensitivity, Specificity, and Accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard Against Conventional Drug-Drug Interactions Clinical Tools.

AI platforms are equipped with advanced ‎algorithms that have the potential to offer a wide range of ‎applications in healthcare services. However, information about the accuracy of AI chatbots against ‎conventional drug-drug interaction tools is limited‎. This study aimed to assess the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard in predicting drug-drug interactions. AI-based chatbots (ie, ChatGPT-3.5, ChatGPT-4, Microsoft Bing AI, and Google Bard) were compared for their abilities to detect clinically relevant DDIs for 255 drug pairs. Descriptive statistics, such as specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), were calculated for each tool. When a subscription tool was used as a reference, the specificity ranged from a low of 0.372 (ChatGPT-3.5) to a high of 0.769 (Microsoft Bing AI). Also, Microsoft Bing AI had the highest performance with an accuracy score of 0.788, with ChatGPT-3.5 having the lowest accuracy rate of 0.469. There was an overall improvement in performance for all the programs when the reference tool switched to a free DDI source, but still, ChatGPT-3.5 had the lowest specificity (0.392) and accuracy (0.525), and Microsoft Bing AI demonstrated the highest specificity (0.892) and accuracy (0.890). When assessing the consistency of accuracy across two different drug classes, ChatGPT-3.5 and ChatGPT-4 showed the highest ‎variability in accuracy. In addition, ChatGPT-3.5, ChatGPT-4, and Bard exhibited the highest ‎fluctuations in specificity when analyzing two medications belonging to the same drug class. Bing AI had the highest accuracy and specificity, outperforming Google's Bard, ChatGPT-3.5, and ChatGPT-4. The findings highlight the significant potential these AI tools hold in transforming patient care. While the current AI platforms evaluated are not without limitations, their ability to quickly analyze potentially significant interactions with good sensitivity suggests a promising step towards improved patient safety.

Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method

The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This paper constructs the single-index nonparametric option pricing model which contains multiple influencing factors and presents the nonparametric estimation form for option butterfly portfolio returns. The empirical analysis shows that the SPD function estimated by using single-index nonparametric option model can effectively calculate the option butterfly portfolio returns with the minimum option strike price interval and provide an effective reference tool for risk-averse investors with limited risk preferences.