Discrimination Index Research Articles

Development of Test Instruments Utilizing Multiple Representations for Enhancing Understanding of Motion Kinematics

Problem-solving in physics remains low due to the limited use of varied or multi-representation test instruments. This study aims to develop a multi-representation-based test instrument on kinematic motion and describe the results of the logical validity and empirical trials of the developed multi-representation-based test instrument for kinematic motion. This research follows a Research and Development (R&D) approach, using stages synthesized from various expert opinions, ranging from needs analysis to test assembly. The research instrument consists of a multi-representation-based test consisting of 30 questions. The data were analyzed using ANATES after being reviewed by three experts. The results indicate that 30 test items were successfully developed, with an average logical validity score of 0.63, demonstrating that the test instrument is valid and feasible for use. Based on statistical analysis, including item validity, difficulty level, discrimination index, and reliability, the empirical validity results show that 11 test items are suitable for use.

Discrimination sensitivity of visual shapes sharpens in autistic adults but only after explicit category learning

BackgroundCategorization and its influence on perceptual discrimination are essential processes to organize information efficiently. Individuals with Autism Spectrum Condition (ASC) are suggested to display enhanced discrimination on the one hand, but also to experience difficulties with generalization and ignoring irrelevant differences on the other, which underlie categorization. Studies on categorization and discrimination in ASC have mainly focused on one process at a time, however, and typically only used either behavioral or neural measures in isolation. Here, we aim to investigate the interrelationships between these perceptual processes using novel stimuli sampled from a well-controlled artificial stimulus space. In addition, we complement standard behavioral psychophysical tasks with frequency-tagging EEG (FT-EEG) to obtain a direct, non-task related neural index of discrimination and categorization.MethodsThe study was completed by 38 adults with ASC and 38 matched neurotypical (NT) individuals. First, we assessed baseline discrimination sensitivity by administering FT-EEG measures and a complementary behavioral task. Second, participants were trained to categorize the stimuli into two groups. Finally, participants again completed the neural and behavioral discrimination sensitivity measures.ResultsBefore training, NT participants immediately revealed a categorical tuning of discrimination, unlike ASC participants who showed largely similar discrimination sensitivity across the stimuli. During training, both autistic and non-autistic participants were able to categorize the stimuli into two groups. However, in the initial training phase, ASC participants were less accurate and showed more variability, as compared to their non-autistic peers. After training, ASC participants showed significantly enhanced neural and behavioral discrimination sensitivity across the category boundary. Behavioral indices of a reduced categorical processing and perception were related to the presence of more severe autistic traits. Bayesian analyses confirmed overall results.LimitationsData-collection occurred during the COVID-19 pandemic.ConclusionsOur behavioral and neural findings indicate that adults with and without ASC are able to categorize highly similar stimuli. However, while categorical tuning of discrimination sensitivity was spontaneously present in the NT group, it only emerged in the autistic group after explicit categorization training. Additionally, during training, adults with autism were slower at category learning. Finally, this multi-level approach sheds light on the mechanisms underlying sensory and information processing issues in ASC.

Psychometric Assessment of Botswana's 2018 Junior Certificate Agriculture Science Items through Item Response Theory

This study aimed to assess the psychometric properties of the Junior Certificate Examination (JCE) agricultural science multiple-choice items in Botswana, using the 3-parameter logistic model (3PLM). Adopting a quantitative descriptive research design, the study analyzed responses from 40,343 examinees who took the 40 multiple-choice items in the 2018 JCE agricultural science examination. The study utilized secondary data from Botswana Examination Council (BEC) records. They also obtained the ethical clearance from the Institutional Review Board of the University Cape Coast. The analysis, conducted using 3PLM within the R-studio environment revealed that 15% of items exhibited poor discrimination indices, 10% had difficulty and 12.5% were prone to guessing. There was an identification of Key anomalies in specific items, warranting careful consideration. Approximately 90% of the multiple-choice items fell within the good difficulty level range but 10% appeared to be poor difficulty. For item discrimination, up to 85% were within the acceptable range while 15% were outside. The study recommends a thorough revision or removal of items with poor parameter estimates, emphasizing continuous monitoring and collaboration between the Ministry of Basic Education and test constructors for ongoing improvement. Additionally, the study recommends stringent review of items with extreme difficulty indices to maintain a balanced assessment.

Item Analysis in Multiple Choice Questions: A Study on Question Difficulty and Authors’ Evaluation

Introduction: Multiple choice questions (MCQs) are widely used in medical education. Difficulty Index (DIF I), Discrimination Index (DI), and Distractor Efficiency (DE) are the most commonly used item analyses. MCQ authors should anticipate the possible difficulty level in writing their questions and carefully arrange distractors. This study aims to evaluate the quality of MCQ and the predictions of MCQ authors about the difficulty levels of their questions. Methods: In this study, the DIF I, DI and DE values of 688 MCQs in the exams held in the first year of the 2021-2022 academic year of Dokuz Eylül University Faculty of Medicine were investigated. The effect of the number of Non-Functional Distractors (NFD) on DIF I and DI, and the compliance between the DIF I predictions of the question authors and the actual values was evaluated. Results: DIF I mean was 0.57±0.21, and 47.5% was at the ideal difficulty level. There was a significant difference between the estimated and the actual DIF I (p=0.002). The DI average was 0.31 ± 0.17, and the discrimination level of 43.6% was excellent. 36.8% of distractors were NFD. MCQ's difficulty and discriminatory ability were significantly different according to the number of NFDs (p=0.000 for all). Discussion: It was determined that the number of NFDs significantly affected difficulty and discriminatory ability. There was a difference between the estimates of the difficulty and the actual values. Reducing the number of options in MCQs and being more careful when crafting questions can improve the quality of the questions.

Evaluating Elasticity-Science Process Skills (E-SPS) in Construction Instrumen through Rasch Modelling

The assessment of material elasticity science process skills in high school students requires validated and reliable instruments. This study aims to develop and validate a set of multiple-choice questions using the Rasch model to ensure their suitability for educational assessments. This quantitative research employed a model approach based on the Rasch model. The instrument consisted of multiple-choice questions designed to evaluate material elasticity science process skills. The validation process included two stages: content validation and construct validation. Content validation involved eight practicing teachers as validators, while construct validation was conducted with a sample of 32 high school students. Data analysis was performed using MNSQ infit, MNSQ outfit, delta, discrimination index, item differential power, and item difficulty level, with the assistance of Quest data analysis software. The results of the analysis from Quest revealed that only 10 questions met the Rasch model criteria. Among these, question number six was identified as the most difficult. The validation process demonstrated that the developed questions are appropriate for assessing the targeted skills based on the Rasch model. The findings indicate that the validated instrument is reliable and suitable for evaluating material elasticity science process skills in high school students. This study underscores the importance of rigorous validation processes in developing educational assessments. The developed multiple-choice questions, validated through the Rasch model, are feasible for use in assessing material elasticity science process skills. Future research should explore the application of this instrument in various educational contexts to further confirm its reliability and effectiveness across different student demographics.

Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation.

To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation. This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome. During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P =0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P =0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P <0.001; integrative discrimination index (IDI), 0.011, P =0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P =0.001; NRI: 0.494, P <0.001; IDI: 0.020, P =0.028). Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.

ChatGPT as a teaching tool: Preparing pathology residents for board examination with AI-generated digestive system pathology tests.

To evaluate the effectiveness of ChatGPT 4 in generating multiple-choice questions (MCQs) with explanations for pathology board examinations, specifically for digestive system pathology. The customized ChatGPT 4 model was developed for MCQ and explanation generation. Expert pathologists evaluated content accuracy and relevance. These MCQs were then administered to pathology residents, followed by an analysis focusing on question difficulty, accuracy, item discrimination, and internal consistency. The customized ChatGPT 4 generated 80 MCQs covering various gastrointestinal and hepatobiliary topics. While the MCQs demonstrated moderate to high agreement in evaluation parameters such as content accuracy, clinical relevance, and overall quality, there were issues in cognitive level and distractor quality. The explanations were generally acceptable. Involving 9 residents with a median experience of 1 year, the average score was 57.4 (71.8%). Pairwise comparisons revealed a significant difference in performance between each year group (P <.01). The test analysis showed moderate difficulty, effective item discrimination (index=0.15), and good internal consistency (Cronbach's α=0.74). ChatGPT 4 demonstrated significant potential as a supplementary educational tool in medical education, especially in generating MCQs with explanations similar to those seen in board examinations. While artificial intelligence-generated content was of high quality, it necessitated refinement and expert review.

Quality of multiple choice question items: item analysis

Background: There are different types of exam formats for educational assessment. Multiple choice questions (MCQs) are frequently utilized assessment tools in health education. Considering the reliability and validity in developing MCQ items is vital. Educators often face the difficulty of developing credible distractors in MCQ items. Poorly constructed MCQ items make an exam easier or too difficult to be answered correctly by students as intended learning objectives. Checking the quality of MCQ items is overlooked and too little is known about it. Therefore, this study aimed to assess the quality of MCQ items using the item response theory model. Methods: A descriptive cross-sectional study was conducted among MCQ items of public health courses administered to 2nd year nursing students at Injibara university. A total of 50 MCQ items and 200 alternatives were evaluated for statistical item analysis. The quality of MCQ items was assessed by difficulty index (DIF), discrimination index (DI), and distractor efficiency (DE) using students’ exam responses. Microsoft excel sheet and SPSS version 26 were used for data management and analysis. Results: Post-exam item analysis showed that 11 (22%) and 22 (44%) MCQs had too difficult and poor ranges for difficulty and discriminating powers respectively. The overall DE was 71.3%. About forty (20%) distractors were non-functional. Only 8 (16%) MCQs fulfilled the recommended criteria for all-DIF, DI, and DE parameters. Conclusions: The desirable criteria for quality parameters of MCQ items were satisfied only in a few items. The result implies the need for quality improvement. Continuous trainings are required to improve the instructors’ skills to construct quality educational assessment tools.

AMPA and NMDA receptors in dentate gyrus mediate memory for sucrose in two port discrimination task.

Although the phenomenon of memory formation and recall associated with the use of psychotropic drugs has been extensively studied, mechanisms underlying memories for natural reward have not been clarified. Herein, we test the hypothesis that glutamatergic receptors in the dentate gyrus play a role in memories associated with sucrose. We used pellet self-administration protocol to generate memories in two-port nose-poke discrimination task using male Wistar rats. During non-rewarded probe trial, the conditioned animals readily discriminated the active port versus inactive port and showed massive increase in mRNA expression of AMPA receptor subunit genes (gria2, gria3) as well as c-Fos protein in the DG. Access to sweet pellet further enhanced c-Fos expression in the DG. However, animals pre-treated with AMPA receptor antagonist CNQX (intra-DG), on exposure to operant chamber (no pellet), showed decreased discrimination as well as c-Fos expression. We suggest that AMPA receptors in DG mediate recall and consolidation of memories associated with sucrose consumption. CNQX pre-treated animals, if presented with sweet pellet on nose poke, exhibited high discrimination index coupled with increased c-Fos expression. In these CNQX treated rats, the DI was again decreased following administration of NMDA receptor antagonist AP5. We suggest that, although AMPA receptors are blocked, the access to sweet pellet may induce surge of glutamate in the DG, which in turn may reinstate memories via activation of erstwhile silent synapses in NMDA dependant manner.

THINKING STYLES INVENTORY ADAPTATION AND STANDARDIZATION IN LATVIAN

Thinking styles are defined as consistent individual differences in preferred ways of organizing and processing information and experience, thus bridging the gap between personality traits and cognitive processes, meaning, it is more important to understand what abilities people prefer to develop and use, rather than what abilities they have. Thinking styles remain an ongoing area of research in education and psychology, providing insights into students’ experiences and informing educational practices. Latvia still faces a shortage of validated and standardized tests, thus to determine thinking styles, a questionnaire titled “Thinking Style Inventory – Revised II” was selected. This research is a part of a much wider study, which is based on the idea that nowadays the use of the internet and technology form and develop young people’s habits of mind that have not yet been explored. Based on this idea, a research project was established whose overall goal is to discover the impact of young people’s (age 16-25) internet use patterns on cognitive functioning (The impact of internet usage patterns on the development of youth’s cognitive styles, No.: lzp-2021/1-0357). The goal of the specific stage of the study and thus this article is to adapt and standardize the Thinking Style Inventory–Revised II in Latvian. To reach the goal, the questionnaire underwent back and forth translation, then was piloted (n=30), checked for internal consistency, thus concluding the first stage of adaptation. For standardization (n=2169) factoranalysis, discrimination indexes and norms were determined. The results show an overall good internal consistency and the factoranalysis shows similar structure to the original questionnaire. The obtained results indicate that Thinking Style Inventory – Revised II Latvian version can be successfully used to determine thinking styles in Latvia.

Does targeted memory reactivation during slow-wave sleep and rapid eye movement sleep have differential effects on mnemonic discrimination and generalization?

Targeted memory reactivation (TMR), or the presentation of learning-related cues during sleep, has been shown to benefit memory consolidation for specific memory traces when applied during non-rapid eye movement (NREM) sleep. Prior studies suggest that TMR during rapid eye movement (REM) sleep may play a role in memory generalization processes, but evidence remains scarce. We tested the hypothesis that TMR exerts a differential effect on distinct mnemonic processes as a function of the sleep state (REM vs. NREM) in which TMR is delivered. Mnemonic discrimination and generalization of semantic categories were investigated using an adapted version of the Mnemonic Similarity Task, before and after sleep. Forty-eight participants encoded pictures from eight semantic categories, each associated with a sound. In the pre-sleep immediate test, they had to discriminate "old" (targets) from "similar" (lures) or "new" (foils) pictures. During sleep, half of the sounds were replayed in slow wave sleep (SWS) or REM sleep. Recognition, discrimination, and generalization memory indices were tested in the morning. These indices did not differ between SWS and REM TMR groups or reactivated and non-reactivated item categories. Additional results suggest a positive effect of TMR on performance for highly similar items mostly relying on mnemonic discrimination processes. During sleep, EEG activity after cue presentation increased in the delta-theta and sigma band in the SWS group, and in the beta band in the REM TMR group. These results do not support the hypothesis of differential processing of novel memory traces when TMR is administered in distinctive physiological sleep states.

Prevalence of dyslipidemia, hypertension and diabetes among tribal and rural population in a south Indian forested region.

While NCDs are in rise globally, tribal and rural populations residing near to reserve forests with limited exposure to modern lifestyles may bear a unique burden. This study investigates the prevalence and risk factors of hypertension, diabetes, and dyslipidemia among these communities. We conducted a cross-sectional study between 2018 and 2020 in the forest-dwelling population of Chamarajanagar, India. Using multistage stratified sampling based on caste and remoteness, we enrolled 608 participants aged over 18 years, including 259 non-tribal and 349 tribal individuals. Data collection includes the administration of STEPS questionnaire and measurement of fasting blood sugar, lipid levels, and blood pressure. The prevalence of diabetes, hypertension, and dyslipidemia were 4.6%, 28.8%, and 85.7%, respectively, among the study population. We also found abnormal levels of low-density lipoprotein (LDL), high-density lipoprotein (HDL), Triglycerides (TGA), Total cholesterol (TC), and very low-density lipoprotein (VLDL)in 4.9%, 82.4%, 22.7%, 5.8%, and 7.4% of participants, respectively. Significant differences were observed in diabetes, LDL abnormality, TGA abnormality, VLDL abnormality, and TC abnormality, but not in hypertension, dyslipidemia, or HDL abnormality, across the Socio Geographic Discrimination Index. We found a significant difference in diabetes and HDL abnormality, but not in hypertension, dyslipidemia, LDL abnormality, TGA abnormality, TC abnormality, or VLDL abnormality, between tribal and non-tribal populations living in the forest-dwelling area. Waist circumference was a significant independent predictor of diabetes among tribal participants, while wealth index, age, and waist circumference were significant predictors of hypertension. There were no significant predictors for dyslipidemia among tribal participants. Our study suggests that tribal population living in a remote area are at a lower risk of developing diabetes compared to non-tribal populations living in the same geographic area. However, the prevalence of hypertension and dyslipidemia among tribal populations remains high and comparable to that of the general population.

An intelligent model for classifying supraventricular tachycardia mechanisms based on 12-lead wearable electrocardiogram devices

To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia (AVNRT) and atrioventricular re-entrant tachycardia (AVRT) using 12-lead wearable electrocardiogram devices. A total of 356 samples of 12-lead supraventricular tachycardia (SVT) electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model, and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October, 2021 to March, 2023 were selected as the testing set. The changes in electrocardiogram parameters before and during induced tachycardia were compared. Based on multiscale deep neural network, an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated. The 3-lead electrocardiogram signals from Ⅱ, Ⅲ, and Ⅴ1 were extracted to build new classification models, whose diagnostic efficacy was compared with that of the 12-lead model. Of the 101 patients with SVT in the testing set, 68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study. The pre-trained model achieved a high area under the precision-recall curve (0.9492) and F1 score (0.8195) for identifying AVNRT in the validation set. The total F1 scores of the lead Ⅱ, Ⅲ, Ⅴ1, 3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597, 0.6061, 0.3419, 0.6003 and 0.6136, respectively. Compared with the 12-lead classification model, the lead-Ⅲ model had a net reclassification index improvement of -0.029 (P=0.878) and an integrated discrimination index improvement of -0.005 (P=0.965). The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Multiple-Choice Questions in Basic Biomedical Science Module

The evaluation process in medical education involves evaluating knowledge, skills, and attitudes based on the achievements and competencies to be achieved. The multiple-choice question (MCQ) instrument is an assessment instrument often used in the medical field. So far, the MCQs have been implemented in Indonesia's national medical competency examination. Furthermore, maintaining the quality of MCQs at the faculty level is very important to maintain the quality of medical graduates. In this study, an evaluation was carried out on 250 items of MCQs in three basic biomedical modules, followed by analyses of the MCQs characteristics, item difficulty index (DIF-I), and item discrimination index (DI). The analysis found that the Kr-20 value was &gt;0.8 in the three modules. Analysis of the item difficulty index (DIF-I) in the ideal category obtained 33 (36.7%), 29 (38.7%), and 34 (39.5%), respectively. The ideal category's item discrimination index (DI) was 63.3%, 77.3%, and 69.4%, respectively. The results of this study illustrate that there are still MCQs that are not ideal and need attention for future improvements. These results prove that more work must be done to improve the standard of MCQs used in medical examinations. Periodic evaluation and training on making standardized multiple-choice question components need to be planned within the faculty.

A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia.

Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = -13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden' s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854-0.987). The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

Low caveolin-1 levels and symptomatic intracranial haemorrhage risk in large-vessel occlusive stroke patients after endovascular thrombectomy.

Caveolin-1 (Cav-1) is reported to mediate blood-brain barrier integrity after ischaemic stroke. Our purpose was to assess the role of circulating Cav-1 levels in predicting symptomatic intracranial haemorrhage (sICH) amongst ischaemic stroke patients after endovascular thrombectomy (EVT). Patients with large-vessel occlusive stroke after EVT from two stroke centres were prospectively included. Serum Cav-1 level was tested after admission. sICH was diagnosed according to the Heidelberg Bleeding Classification. Of 325 patients (mean age 68.6 years; 207 men) included, 47 (14.5%) were diagnosed with sICH. Compared with patients without sICH, those with sICH had a lower concentration of Cav-1. After adjusting for potential confounders, multivariate regression analysis demonstrated that the increased Cav-1 level was associated with a lower sICH risk (odds ratio 0.055; 95% confidence interval 0.005-0.669; p = 0.038). Similar results were obtained when Cav-1 levels were analysed as a categorical variable. Using a logistic regression model with restricted cubic splines, a linear and negative association of Cav-1 concentration was found with sICH risk (p = 0.001 for linearity). Furthermore, the performance of the conventional risk factors model in predicting sICH was substantially improved after addition of the Cav-1 levels (integrated discrimination index 2.7%, p = 0.002; net reclassification improvement 39.7%, p = 0.007). Our data demonstrate that decreased Cav-1 levels are related to sICH after EVT. Incorporation of Cav-1 into clinical decision-making may help to identify patients at a high risk of sICH and warrants further consideration.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

BackgroundPancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models. MethodsTo identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models. ResultsWe identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154). ConclusionThe five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

CatLet score and clinical CatLet score as predictors of long-term outcomes in patients with acute myocardial infarction presenting later than 12 hours from symptom onset

Background Our recently developed C oronary A rtery T ree description and L esion E valua T ion (CatLet) angiographic scoring system is unique in its description of the variability in the coronary anatomy, the degree of stenosis of a diseased coronary artery, and its subtended myocardial territory, and can be utilized to predict clinical outcomes for patients with acute myocardial infarction (AMI) presenting ≤12 h after symptom onset. The current study aimed to assess whether the Clinical CatLet score (CCS), as compared with CatLet score (CS), better predicted clinical outcomes for AMI patients presenting >12 h after symptom onset. Methods CS was calculated in 1018 consecutive AMI patients enrolled in a retrospective registry. CCS was calculated by multiplying CS by the ACEF I score (age, creatinine, and left ventricular ejection fraction). Primary endpoint was major adverse cardiac events (MACEs) at 4-year-follow-up, a composite of cardiac death, myocardial infarction, and ischemia-driven revascularization. Results Over a 4-year follow-up period, both scores were independent predictors of clinical outcomes after adjustment for a broad spectrum of risk factors. Areas-under-the-curve (AUCs) for CS and CCS were 0.72(0.68–0.75) and 0.75(0.71–0.78) for MACEs; 0.68(0.63–0.73) and 0.78(0.74–0.83) for all-cause death; 0.73(0.68–0.79) and 0.83(0.79–0.88) for cardiac death; and 0.69(0.64–0.73) and 0.75(0.7–0.79) for myocardial infarction; and 0.66(0.61–0.7) and 0.63(0.58–0.68) for revascularization, respectively. CCS performed better than CS in terms of the above-mentioned outcome predictions, as confirmed by the net reclassification and integrated discrimination indices. Conclusions CCS was better than CS to be able to risk-stratify long-term outcomes in AMI patients presenting >12 h after symptom onset. These findings have indicated that both anatomic and clinical variables should be considered in decision-making on management of patients with AMI presenting later.

Serum lipopolysaccharide associated with new-onset atrial fibrillation in patients with non-small-cell lung cancer a retrospective observational study.

Lipopolysaccharide (LPS) is related to atrial fibrillation (AF). But so far, the relationship between LPS and new-onset AF (NOAF) in patients with lung cancer is unrevealed. This study was to investigate the association between LPS and NOAF in patients after lung cancer surgery. This was a single-center retrospective clinical observational study. Patients diagnosed with non-small-cell lung cancer (NSCLC) were enrolled. All patients receiving lung cancer surgery and at least 24 h electrocardiogram (ECG) examination was recorded during the hospitalization. The incidence of NOAF in this study was 34/406 (8.4%). The univariate analysis showed that NOAF was associated with age, intraoperative blood transfusion (IBT), chronic obstructive pulmonary disorder (COPD), and LPS. After adjusting risk factors, it was found that age, IBT and LPS (OR, 1.031; 95% CI: 1.001-1.042; P = 0.002) were still risk factors for NOAF. The area under curve (AUC) value was 0.709 for the LPS. When the LPS was added to the conventional model, the Net reclassification index (NRI) and integrated discrimination index (IDI) were improved significantly. Elevated LPS is associated with an increased risk of NOAF in patients after lung cancer surgery. LPS contributed to the discrimination of the NOAF risk model and improved it markedly.

Validity evidence for a novel multiple-choice question bank in the field of child abuse pediatrics

BackgroundThough child abuse pediatrics has been a board-certified subspecialty for 15 years, there are few formalized board preparation resources available. ObjectiveThe purpose of this project was to establish a multiple-choice question bank with sufficient validity evidence for use in preparation for the child abuse pediatrics board examination. Participants and settingThe question bank was distributed via an electronic child abuse pediatrics mailing list. Participants completing the entire question bank included 27 board-certified child abuse pediatricians (CAPs), 19 board-eligible CAPs, and 18 CAP fellows. MethodsWe used Messick's framework to conduct the validity investigation, which includes five components: content evidence, response process, internal structure, relation to other variables, and consequences. Item analyses included difficulty index, discrimination index, and distractor analysis. We used Cronbach's alpha to estimate internal consistency reliability. We conducted linear regressions of scores on the question bank compared to in-training exam scores and career stage. ResultsEighty-four participants completed part of the question bank, and 64 completed the entire question bank. Of the original 117 questions (“items”), 94 met inclusion criteria. The mean score among board-certified CAPs was 80 %, and among participants reporting passing third-year ITE scores was 81 %. Correlation coefficient of scores on this question bank by career stage was r = 0.94, and by year of fellowship was r = 0.99. Cronbach's alpha for internal consistency reliability was 0.83. ConclusionsThis multiple-choice question bank is the first question bank with a robust validity investigation for use by child abuse pediatrics trainees.