Retrieval augmented scientific claim verification.

Objective: To automate scientific claim verification using PubMed abstracts. Materials and Methods: We developed CliVER, an end-to-end scientific Claim VERification system that leverages retrieval-augmented techniques to automatically retrieve relevant clinical trial abstracts, extract pertinent sentences, and use the PICO framework to support or refute a scientific claim. We also created an ensemble of three state-of-the-art deep learning models to classify rationale of support, refute, and neutral. We then constructed CoVERt, a new COVID VERification dataset comprising 15 PICO-encoded drug claims accompanied by 96 manually selected and labeled clinical trial abstracts that either support or refute each claim. We used CoVERt and SciFact (a public scientific claim verification dataset) to assess CliVER's performance in predicting labels. Finally, we compared CliVER to clinicians in the verification of 19 claims from 6 disease domains, using 189 648 PubMed abstracts extracted from January 2010 to October 2021. Results: In the evaluation of label prediction accuracy on CoVERt, CliVER achieved a notable F1 score of 0.92, highlighting the efficacy of the retrieval-augmented models. The ensemble model outperforms each individual state-of-the-art model by an absolute increase from 3% to 11% in the F1 score. Moreover, when compared with four clinicians, CliVER achieved a precision of 79.0% for abstract retrieval, 67.4% for sentence selection, and 63.2% for label prediction, respectively. Conclusion: CliVER demonstrates its early potential to automate scientific claim verification using retrieval-augmented strategies to harness the wealth of clinical trial abstracts in PubMed. Future studies are warranted to further test its clinical utility.

Large language models: a primer and gastroenterology applications.

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.

Large language models in gastroenterology: a simplified overview for clinicians This text discusses the recent advancements in large language models (LLMs), like ChatGPT, which have significantly advanced artificial intelligence. These models can create specific, high-quality text responses without needing extensive training data or complex programming. They show great promise in transforming various aspects of clinical healthcare, particularly in improving patient care, medical education, and research. This article focuses on how LLMs can be applied in the field of gastroenterology. It explains the technical aspects of LLMs, their strengths and weaknesses, and how to use them effectively and safely. The text also explores how LLMs could be used in clinical practice, education, and research in gastroenterology. Finally, it discusses the challenges in implementing these models and the ongoing efforts to overcome them, aiming to provide gastroenterologists with the basic knowledge needed to engage more actively in the development and use of this emerging technology.

Understanding conflicts of interest in rational drug prescription in a developing country: A stakeholder analysis, healthcare guidelines and ethical public health issues.

Background: Rational drug prescription (RDP) is one of the main components of the healthcare systems. Irrational prescribing can bring about numerous negative consequences for the patients and governmental agencies. This study aims to analyze the involvement of stakeholders in rational drug prescribing, their position (opponent or proponent), and the rationale behind it. Methods: This was a qualitative study conducted in 2019. Semi-structured face-to-face interviews were conducted with 40 stakeholders. Purposive and snowball sampling techniques with maximum heterogeneity were adopted to select the interviewees. Data was analyzed by MAXQDA software using thematic approach. Results: Iranian Food and Drug Administration employs the highest authority on the rational prescribing policy. Although the Ministry of Health and Medical Education, the Social Security Organization as one of the main health insurance organizations, pharmaceutical companies, and the Medical Council of the Islamic Republic of Iran, are among agencies that have great authority to improve rational prescribing, they fail to act professionally as they have conflicting interests. Remarkably, the Iran Food and Drug Administration, insurance organizations, family physicians, and patients, highly support the rational prescribing policy while the pharmaceutical companies display the least support for it. Conclusions: To make the prescription and using drugs more rational, policy makers should focus on different sources of conflicts of interest that different actors have. They should devise legal, behavior and financial policies accordingly to lessen or at least neutralize these conflicting interests, otherwise achieving RDP would be impossible in short and long terms.

Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments.

The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models' consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.

Evaluating large language models on medical evidence summarization.

Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.

Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics.

Objectives: To develop an automated natural language processing (NLP) method for extracting high-fidelity Barrett's Esophagus (BE) endoscopic surveillance and treatment data from the electronic health record (EHR). Methods: Patients who underwent BE-related endoscopies between 2016 and 2020 at a single medical center were randomly assigned to a development or validation set. Those not aged 40 to 80 and those without confirmed BE were excluded. For each patient, free text pathology reports and structured procedure data were obtained. Gastroenterologists assigned ground truth labels. An NLP method leveraging MetaMap Lite generated endoscopy-level diagnosis and treatment data. Performance metrics were assessed for this data. The NLP methodology was then adapted to label key endoscopic eradication therapy (EET)-related endoscopy events and thereby facilitate calculation of patient-level pre-EET diagnosis, endotherapy time, and time to CE-IM. Results: 99 patients (377 endoscopies) and 115 patients (399 endoscopies) were included in the development and validation sets respectively. When assigning high-fidelity labels to the validation set, NLP achieved high performance (recall: 0.976, precision: 0.970, accuracy: 0.985, and F1-score: 0.972). 77 patients initiated EET and underwent 554 endoscopies. Key EET-related clinical event labels had high accuracy (EET start: 0.974, CE-D: 1.00, and CE-IM: 1.00), facilitating extraction of pre-treatment diagnosis, endotherapy time, and time to CE-IM. Conclusions: High-fidelity BE endoscopic surveillance and treatment data can be extracted from routine EHR data using our automated, transparent NLP method. This method produces high-level clinical datasets for clinical research and quality metric assessment.

Evaluating Large Language Models on Medical Evidence Summarization.

Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study has demonstrated that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.

Sex and smoking differences in the association between gastroesophageal reflux and risk of esophageal squamous cell carcinoma in a high-incidence area: Golestan Cohort Study.

Prior studies have conflicting findings regarding the association between gastroesophageal reflux disease (GERD) and esophageal squamous cell carcinoma (ESCC). We examined this relationship in a prospective cohort in a region of high ESCC incidence. Baseline exposure data were collected from 50 045 individuals using in-person interviews at the time of cohort entry. Participants were followed until they developed cancer, died, or were lost to follow up. Participants with GERD symptoms were categorized into any GERD (heartburn or regurgitation), mixed symptoms, or heartburn alone. Multivariable Cox regression was used to assess the relationship between GERD symptom group and histologically confirmed ESCC. The model was adjusted for known risk factors for GERD and ESCC. 49 559 individuals were included in this study, of which 9005 had GERD symptoms. Over 13.0 years of median follow up, 290 individuals were diagnosed with ESCC. We found no association between any GERD and risk of ESCC (aHR 0.90, 95% CI: 0.66-1.24, P = .54). Similar findings were observed for the GERD symptom subtypes. Significant interactions between any GERD and sex (P = .013) as well as tobacco smoking (P = .028) were observed. In post-hoc analyses, GERD was associated with a decreased risk of ESCC in men (aHR 0.51, 95% CI: 0.27-0.98 P = .04) and in smokers (aHR 0.26, 95% CI: 0.08-0.83 P = .02). While there was little evidence for an overall association between GERD symptoms and ESCC risk, significant interactions with sex and smoking were observed. Men and smokers with GERD symptoms had a lower risk of ESCC development.

Comparing the Associated Factors on Lifestyle Between Type 2 Diabetic Patients and Healthy People: A Case-Control Study.

BACKGROUND: the most important way to control diabetes is to follow a preventive lifestyle and if a diabetic individual follows a preventive lifestyle which he or she has accepted. The main objective of the current study is to compare the factors affecting the lifestyle in patients suffering from Type II diabetes and the healthy individuals in Kermanshah City. METHODS: this study is based on a case-control design where using simple random sampling, 110 patients suffering from type II diabetes are selected as the case group and 111 healthy subjects among the companions of other patients are selected as the control group from the Center for Diabetics in Kermanshah City. The average age of the participants is 48.8±11.0. The questionnaires used for collecting the data included the following: the demographic information questionnaire and the lifestyle questionnaire which covers diet, physical activity, coping with stress, and smoking. Software applications including STSTA14 and SPSS23 were used for performing statistical computations and logistic regression or linear regression tests were used for analyzing the collected data. RESULTS: in the subscales of diet, physical activity, spiritual growth, and stress management, there was a significant difference between the diabetic and healthy groups in a wat that the average score for these subscales was higher in the healthy individuals. While the average score for "health responsibility" was higher in the diabetic group compared to the healthy subjects, the difference was not significant (P<0.232). Moreover, there was a significant statistical relationship between the two groups, i.e. the diabetic and healthy groups, and the variables of age, education level, and occupation (P > 0.05). CONCLUSION: healthy lifestyle including proper diet and athletic activity is effective in preventing type II diabetes. Accordingly, implementing policies in the urban transportation system such as providing a special lane for bikers in the cities, increasing the tax for harmful foods, considering subsidies for healthy food products, and self-care of individuals can be effective.

Explore of the reasons of irrational prescribing in Iran: A qualitative study.

Background: Irrational prescribing is highly prevalent in Iran, and it is under the impact of different factors. Objective: This research aims to recognize the reasons for Irrational prescribing in Iran. Methods: A qualitative approach and a conventional content analysis were employed to perform this research. The research community includes some experts and key specialists in medication prescribing. Semi-structured interviews were used for data collection. The intentional sampling method was applied, and theoretical saturation was reached by conducting 40 interviews with experts. The data analysis process was done following the steps suggested by Graneheim and Lundman. Results: Four main categories and 12 subcategories appeared after analyzing the data. The main categories are organizational and management factors, legal factors, cultural factors, and economic factors. Conclusion: Irrational prescribing can be prevented by reforming the referral system, overseeing pharmacies and physicians, raising public awareness and correcting their misconceptions about the medications, and creating an appropriate mechanism for pricing and selling medicines.

Rare Complication of Endoscopic Variceal Therapy: Wide-Complex Tachycardia Associated With Embolization of Glue and Coil.

We present the case of a woman with upper gastrointestinal bleeding secondary to gastric varices requiring endoscopic cyanoacrylate glue and coil embolization. The procedure was complicated by regular, wide-complex tachycardia, with further investigation revealing cardiopulmonary migration of the glue and coil. (Level of Difficulty: Advanced.).

The Reasons for Self-Medication from the Perspective of Iranian Nursing Students: A Qualitative Study.

Background: The prevalence of self-medication has increased dramatically worldwide. This study was conducted to determine the reasons for self-medication from the perspective of Iranian nursing students. Methods: This qualitative study was conducted using the content analysis method. Fifteen nursing students were selected by the purposeful sampling method. Data were collected by in-depth semistructured interviews. Qualitative content analysis method was used for data analysis. The MAXQDA software was used for data management. Results: Data saturation was achieved with fifteen interviews with nine women and six men, with a mean age of 26.5 ± 4.8 years. The reasons for self-medication were explained in five categories and fifteen subcategories. Some of the reasons for self-medication were having medication information, having previous experience, easy access to medicine, lack of enough time, access to medical staff, cost of a doctor's visit, inadequate respect for patient privacy, pharmaceutical advertising in the media, and information explosion. Discussion. Several factors are involved in self-medication. Given the dangers of self-medication, health policymakers must adopt strict policies for pharmacies that sell drugs without a prescription. Furthermore, it is helpful to run training courses on self-medication risks for students.

Risk Factors Associated With SARS-CoV-2 Breakthrough Infections in Fully mRNA-Vaccinated Individuals: Retrospective Analysis.

BACKGROUND: COVID-19 messenger RNA (mRNA) vaccines have demonstrated efficacy and effectiveness in preventing symptomatic COVID-19, while being relatively safe in trial studies. However, vaccine breakthrough infections have been reported. OBJECTIVE: This study aims to identify risk factors associated with COVID-19 breakthrough infections among fully mRNA-vaccinated individuals. METHODS: We conducted a series of observational retrospective analyses using the electronic health records (EHRs) of the Columbia University Irving Medical Center/New York Presbyterian (CUIMC/NYP) up to September 21, 2021. New York City (NYC) adult residences with at least 1 polymerase chain reaction (PCR) record were included in this analysis. Poisson regression was performed to assess the association between the breakthrough infection rate in vaccinated individuals and multiple risk factors-including vaccine brand, demographics, and underlying conditions-while adjusting for calendar month, prior number of visits, and observational days in the EHR. RESULTS: The overall estimated breakthrough infection rate was 0.16 (95% CI 0.14-0.18). Individuals who were vaccinated with Pfizer/BNT162b2 (incidence rate ratio [IRR] against Moderna/mRNA-1273=1.66, 95% CI 1.17-2.35) were male (IRR against female=1.47, 95% CI 1.11-1.94) and had compromised immune systems (IRR=1.48, 95% CI 1.09-2.00) were at the highest risk for breakthrough infections. Among all underlying conditions, those with primary immunodeficiency, a history of organ transplant, an active tumor, use of immunosuppressant medications, or Alzheimer disease were at the highest risk. CONCLUSIONS: Although we found both mRNA vaccines were effective, Moderna/mRNA-1273 had a lower incidence rate of breakthrough infections. Immunocompromised and male individuals were among the highest risk groups experiencing breakthrough infections. Given the rapidly changing nature of the SARS-CoV-2 pandemic, continued monitoring and a generalizable analysis pipeline are warranted to inform quick updates on vaccine effectiveness in real time.

Comparative Evaluation of the Effect of Aloe Vera Gel, Olive Oil, and Compound Aloe Vera Gel-Olive Oil on Prevention of Pressure Ulcer: A Randomized Controlled Trial.

Background: One of the most common problems in the intensive care units (ICUs) is pressure ulcers (PUs). The present study aimed to evaluate the effectiveness of aloe vera gel, olive oil, and compound aloe vera gel-olive oil in the prevention of PUs. Materials and Methods: This randomized clinical trial was conducted on 240 patients. They were randomly divided into four groups, aloe vera gel (n = 60), olive oil (n = 60), aloe vera gel-olive oil combination (n = 60), and control (n = 60). Braden scale and National Pressure Ulcer Advisory Panel scale were used to collect data. The intervention was performed for 30 days. In the intervention and control groups, the patient received routine care. In each intervention group, 10-15 ml of olive oil or aloe vera gel or a combination of olive oil and aloe vera was rubbed into body areas under pressure. Results: There were no PUs detected in all groups before the intervention; after the intervention, 12 patients in the olive group, 20 patients in the aloe vera group, 10 patients in the aloe vera-olive combination group, and 22 patients in the control group developed PUs. The results reported 40% of the patients with stage 1 PU and 10% of them with stage 2. Conclusion: Due to the effectiveness of olive oil and aloe vera-olive oil combination in preventing PU, it is recommended to use these herbal compounds in preventing PU on ICU patients.

Examining Lifestyle Components in Hospitalized COVID-19 Patients, Iran 2021.

Background: COVID-19 pandemic is a serious health threating element throughout the world. One of the key elements to strengthen the body's immune system is to follow a healthy lifestyle to deal with health threating. The aim of this study was to evaluate the lifestyle components in COVID-19 patients. Methods: This descriptive-analytical study carried on hospitalized COVID-19 patients from October 22, 2020 to January 19, 2021. Demographic characteristics, physical activity, nutritional status, stress and anxiety, and substance abuse were assessed. A simple model and multiple logistic regression model were used. Results: About 32% were hospitalized in the intensive care unit (ICU). Healthy lifestyle was observed only in 28%. About 82% had insufficient physical activity, and 67.3% was reported to be unfavorable in nutritional status. Severe stress and anxiety were observed in 30.4% of people. There were significant relationships between age (AOR = 2.11, p = 0.036), education (AOR = 0.35, p = 0.002) and a healthy lifestyle. A significant correlation was observed between ICU admission and unhealthy lifestyle (AOR = 0.40, p = 0.015). Conclusion: Unhealthy lifestyle behaviors were seen in the most COVID-19 patients. Considering the significance of lifestyle changes could prove effective in reducing the risk of transmissible viral infections.

Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.

OBJECTIVE: To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials. MATERIALS AND METHODS: Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk. RESULTS: Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure. CONCLUSIONS: Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.

The relationship of dietary total antioxidant capacity with sarcopenia and cardiometabolic biomarkers in type 2 diabetes patients.

BACKGROUND: The aim of this study was to investigate the relationship of dietary total antioxidant capacity (DTAC) with sarcopenia and metabolic biomarkers in people with type 2 diabetes in the Kurdish race. METHODS: In this cross-sectional study, data of 189 type 2 diabetic patients (35-65 years old) from RaNCD cohort study were evaluated. DTAC, fasting blood sugar, lipid profile, body composition, muscle strength, and sarcopenia were assessed. t and χ2 tests to compare the variables between sarcopenic and non-sarcopenic patients and one-way analysis of variance to compare the variables in DTAC tertiles were used. The relationship between DTAC and different variables was evaluated using multiple logistic regression model. RESULTS: The mean age and body mass index were 49.7 ± 8.7 years and 27.1 ± 3.9 kg/m2 . Body mass index, waist circumference, and hip circumference were significantly different between diabetic patients with and without sarcopenia (p < 0.05). In crude (p = 0.010) and adjusted (p = 0.035) models, there was a significant relationship between DTAC and fasting blood sugar. Also, the relationship between DTAC with waist (p = 0.019) and hip (ß = -4.25, p = 0.026) circumference was significant. Sarcopenia was significantly lower in the third tertile in comparison with the first tertile of DTAC (p = 0.016).  CONCLUSION: Diet with higher DTAC can be associated with lower fasting blood sugar, abdominal obesity and sarcopenia in type 2 diabetic patients. However, further studies are required to confirm these relationships.

Non-Acid Fluid Exposure and Esophageal Squamous Cell Carcinoma.

Esophageal squamous cell carcinoma (ESCC) accounts for the large majority of esophageal cancer cases worldwide. In this review, we examine the potential role of non-acidic fluid (NAF) exposure in ESCC carcinogenesis. Esophageal NAF consists of a mixture of salivary, esophageal, gastric, and duodenal fluids, containing inflammatory constituents such as digestive enzymes and bile acids that induce DNA damage, as well as known carcinogens such as acetaldehyde and N-nitrosamines. Exposure to NAF can occur in the setting of increased non-acid reflux, decreased gastric acidity, and decreased esophageal fluid clearance. Non-acid reflux has been associated with ESCC in small observational studies, and in animal models bile reflux can promote the development of ESCC. Associations have been found between increased ESCC risk and atrophic gastritis, a history of partial gastrectomy, and proton pump inhibitor use, all of which raise the pH of refluxate. Additionally, a minimally or non-acidic gastric environment contains an altered microbiome that can increase the production of acetaldehyde and N-nitrosamines. Esophageal motility disorders such as achalasia and opioid-induced esophageal dysfunction result in increased stasis and exposure to these potentially proinflammatory constituents of NAF. NAF may promote the development of ESCC via multiple mechanisms and is an understudied area of research.

The Survey of Knee Osteoarthritis in the Population over Age 50 Visited in the Health Bus in Kermanshah, Iran.

Along with an aging population worldwide, knee osteoarthritis (KOA), which is the main cause of musculoskeletal pain and disability in the elderly and decreases the quality of life, is prevalent, and their impact is widespread. This study aimed to evaluate the knee osteoarthritis status among the population over age 50 in Kermanshah, Iran. The research community consisted of the population who has been visited in the health bus in Kermanshah in 2016-2017, of which 589 were chosen by an available sampling method. A WOMAC questionnaire was used to determine the prevalence of knee osteoarthritis. The prevalence of knee stiffness rate after sitting, lying down, or resting during the day among women and men were 40.7% and 20.5%, respectively. According to the findings, the highest prevalence rate of knee pain was in subjects with a BMI higher than 30 (31.6%) and BMI 25-30 (24.5%). 39.2% of the subjects never experienced knee pain, 16.6% monthly, 13.4% once a week, 20.4% daily, and 10.4% of them had prolonged knee pain experience. The prevalence of gender-based knee pain was 60.5% among women and 38.6% among men. 30.5% of women and 61.4% of men never experienced knee pain.

Evaluation of the educational services quality from the viewpoint of postgraduate students at Kermanshah University of medical sciences in 2019.

BACKGROUND: At present, the SERVQUAL model is one of the most significant tools for measuring customers' expectations and perceptions in organizations. Determination of expectations and perceptions of students who are the main university customers from educational services can provide valuable information to the programmers to improve the condition of educational services. Therefore, this study aims to measure the quality of education services from the viewpoint of postgraduate students at Kermanshah Medical Sciences University using the SERVQUAL model in 2019. MATERIALS AND METHODS: This was a descriptive cross-sectional study. The population under the study consisted of 162 students at the postgraduate education section (Master's degree and Ph.D.) studying in the second semester of the academic year of 2019 at Kermanshah Medical Sciences University. The standard SERVQUAL questionnaire was used for data collection, which included five dimensions of the quality of educational services, and the random-stratified sampling method was employed. The data were analyzed using the SPSS Statistics software (version 23.0, SPSS Inc., Chicago, IL, USA), and descriptive and other statistical tests, including the Pearson correlation coefficient, and paired and independent t-tests were performed. RESULTS: Based on the students' perspective in the provision of service there was quality gap existed in all services, and the largest gap was in the reliability dimension (-0.37), and after that, empathy (-0.37), guaranty (0.27), and responsiveness (-0.24) dimensions, respectively, and the lowest one was in the tangible dimension (0.15). There was a statistically significant observed difference between the quality gap in different educational dimensions (P > 0.001). CONCLUSION: Research findings showed that students were not meeting their expectations from the presented educational services. Hence, holding a training workshop is suggested in the field of how to serve and enhance communication skills for employees and teachers.