How to Harness the Power of GPT for Scientific Research: A Comprehensive Review of Methodologies, Applications, and Ethical Considerations.

The rapid advancements in natural language processing, particularly with the development of Generative Pre-trained Transformer (GPT) models, have opened up new avenues for researchers across various domains. This review article explores the potential of GPT as a research tool, focusing on the core functionalities, key features, and real-world applications of the GPT-4 model. We delve into the concept of prompt engineering, a crucial technique for effectively utilizing GPT, and provide guidelines for designing optimal prompts. Through case studies, we demonstrate how GPT can be applied at various stages of the research process, including literature review, data analysis, and manuscript preparation. The utilization of GPT is expected to enhance research efficiency, stimulate creative thinking, facilitate interdisciplinary collaboration, and increase the impact of research findings. However, it is essential to view GPT as a complementary tool rather than a substitute for human expertise, keeping in mind its limitations and ethical considerations. As GPT continues to evolve, researchers must develop a deep understanding of this technology and leverage its potential to advance their research endeavors while being mindful of its implications.

Evaluation of scientific research efficiency based on Data Envelopment Analysis in a third-class hospital in Xinjiang / 中华医学科研管理杂志

Objective:To understand the overall development and dynamic change trend of scientific research input-output efficiency of a tertiary hospital in Xinjiang from 2016 to 2021, so as to provide reference and basis for optimizing hospital scientific research configuration and improving scientific research efficiency.Methods:The BCC model of Data Envelopment Analysis (DEA) method is combined with Malmquist index method for analysis and evaluation.Results:Within 6 years, the average value of comprehensive technical efficiency of nursing units has not reached 1.The total factor productivity of nursing units was unstable, showing an alternating rise and fall trend, with a fluctuation range of 0.749~1.140.Conclusions:The overall efficiency of scientific research in clinical departments of hospitals is not high. Hospitals should implement multi-level scientific research evaluation, form a dynamic evaluation system for scientific research, reasonably optimize and supervise the investment and utilization of scientific research resources, improve the scientific research management system, enhance the scientific research awareness of researchers, promote technological progress of researchers, and thereby improve scientific research efficiency.

Evaluation of scientific research work efficiency of departments in a general hospital based on non-radial super-SBM model / 中华医学科研管理杂志

Objective:Established a Super-DEA model to calculate the efficiency of scientific research work of the hospital departments, statistically compared it with the evaluation results of scientific research work in discipline construction, and proposed improvement suggestions.Methods:Considering the characteristics of the input and output indicators, non-radial super-SBM model from the DEA methods was established to calculate the scientific research efficiency of 47 departments of the hospital in 2019, two independent sample t-tests were used to compare the differences in subject construction scores between effective and ineffective departments with super-efficiency values.Results:The average scientific research efficiency of 47 departments in the hospital was 0.70, with the highest efficiency being the scientific research department and the lowest being the internal medicine department; there were 18 DEA-effective departments and 29 DEA-ineffective departments; among the DEA-ineffective departments, the average human resource input was 1.13 people, the redundant scientific research funds is 854, 800 yuan on average; compared with the evaluation results of scientific research work in the discipline construction in 2019, the ranking of the same discipline is significantly different, the t-test results showed that there was no significant difference in the subject construction scores between DEA effective and ineffective departments. Using this model to calculate the data in 2021, the results show that DEAs in 21 departments are effective, with an effective rate of 42.86%.Conclusions:There is a big difference in the scientific research efficiency of the hospital departments, and the overall efficiency need to be improved; it should be improved according to the reasons for the lack of scientific research efficiency; the discipline construction evaluation system has not considered the input and other deficiencies, it is recommended to optimize the evaluation system to improve fairness and rationality. The results preliminarily show that the model can play a positive guiding role in the scientific research work of the department.

Optimizing Research Output: How Can Psychological Research Methods Be Improved?

Recent evidence suggests that research practices in psychology and many other disciplines are far less effective than previously assumed, which has led to what has been called a "crisis of confidence" in psychological research (e.g., Pashler & Wagenmakers 2012). In response to the perceived crisis, standard research practices have come under intense scrutiny, and various changes have been suggested to improve them. The burgeoning field of metascience seeks to use standard quantitative data-gathering and modeling techniques to understand the reasons for inefficiency, to assess the likely effects of suggested changes, and ultimately to tell psychologists how to do better science. We review the pros and cons of suggested changes, highlighting the many complex research trade-offs that must be addressed to identify better methods.

Assessment of research efficiency of critical care medicine in hospitals affiliated S university based on data envelopment method: taking the 13th Five-Year Plan period as an example / 中华危重病急救医学

Objective:To investigate the static and dynamic trends of scientific research efficiency of the critical care medicine in hospitals affiliated S university during the 13th Five-Year Plan period.Methods:Based on the scientific research data of 16 hospitals affiliated to Beijing S University from 2014 to 2020, the scientific research investment funds and the number of physicians involved in scientific research were selected as input evaluation indexes, and the number of science citation index (SCI) papers, Chinese science citation database (CSCD) papers, and the number of masters and doctors trained were selected as output evaluation indexes, and the evaluation index system of scientific research efficiency of critical care medicine was constructed. SPSS version 23.0 software was used for descriptive data statistics, and data envelopment analysis (DEA)-BCC model and DEA-Malmquist index model of DEAP 2.1 software were used for static and dynamic evaluation of its scientific research efficiency from 2016 to 2020, respectively.Results:① The scientific research technical efficiency (TE) of critical care medicine in 16 hospitals affiliated with S universities varied greatly from 2016 to 2020, but pure technical efficiency (PTE) and scale efficiency (SE) were at a good level, and 6-11 affiliated hospitals in critical care medicine kept DEA effective for 5 consecutive years. ② Dynamic analysis of their total factor productivity (TFP) of scientific research from 2016 to 2020 showed a trend of rising and then falling and then rising again. The mean value was 0.985. The technical efficiency change (TEC) showed a decreasing and then increasing trend, and the technical progress change (TC) showed a slow increasing and then decreasing trend, with a mean value of 0.953. While the mean values of TEC, pure technical efficiency change (PTEC) and scale efficiency change (SEC) were above 1, which showed that the growth of total factor productivity index of research and innovation depended more on the technical efficiency index.Conclusions:The "gain effect" and "catch-up effect" of scientific research efficiency in the specialty of critical care medicine in hospitals affiliated S universities are obvious, but the "growth effect" is not obvious. "Although the research efficiency of the 13th Five-Year Plan period has been significantly improved, there is still much room for improvement in scientific and technological innovation and international academic influence.

The evaluation of scientific research efficiency in a tertiary hospital based on Data Envelopment Analysis: Application analysis with Slack Based Measure model / 中华医学科研管理杂志

Objective:To evaluate the Scientific Research Efficiency in A Tertiary Hospital, and provide recommendations for further improvement based on evaluation results.Methods:We evaluated the scientific research efficiency of 35 disciplines in a Tertiary Hospital from 2015 to 2017 with Slack Based Measure based Data envelopment analysis(DEA).Results:The average efficiency value from 2015 to 2017 is 0.44-0.65.The efficiency of Scientific Research from high to low was: supportive department, surgery department, internal medicine.There are 13 disciplines (37.14%) which efficiency increases gradually and 7 discipline (20%) which efficiency decreases.9 disciplines are DEA valid (efficiency=1) for at least 2 years and 9 is DEA invalid (efficiency<1) in three years.The average slack variable of researchers is 18.6 while average slack variable of input scientific research fund is 23.38 million RMB.Conclusions:The average efficiency of the tertiary hospital shows a rising trend.However, the efficiency of disciplines varies largely.For DEA invalid disciplines, it is important to improve efficiency by increasing output and improving efficiency of scientific research funding.

The evaluation of scientific research efficiency in a tertiary hospital based on Data Envelopment Analysis and Malmquist index / 中华医学科研管理杂志

Objective:To evaluate the scientific research efficiency and its change in a tertiary hospital, provide recommendations for improvement based on evaluation findings.Methods:We evaluated the scientific research efficiency of 35 disciplines in a tertiary hospital from 2015 to 2017 with BCC model of Data Envelopment Analysis and Malmquist index.Results:The number of DEA valid disciplines was 10, 16, 10 respectively. The average efficiency value from 2015 to 2017 of internal medicine, surgery department and supportive department was 0.62, 0.71, 0.74. The Total Factor Productivity of 2017, compared with 2015, was 1.30, which was mainly attributed to increase of Pure Technical Efficiency. There were 23 disciplines which efficiency increased and 12 disciplines which efficiency decreased.Conclusions:The scientific research efficiency of the tertiary hospital showed a rising trend. The efficiency of disciplines varied largely. For disciplines of DEA invalid or with downward trend, it is important to improve efficiency by improving management, cultivating talents, improving technology and increasing scientific output, and so on.

Automation in the Life Science Research Laboratory.

Protocols in the academic life science laboratory are heavily reliant on the manual manipulation of tools, reagents and instruments by a host of research staff and students. In contrast to industrial and clinical laboratory environments, the usage of automation to augment or replace manual tasks is limited. Causes of this 'automation gap' are unique to academic research, with rigid short-term funding structures, high levels of protocol variability and a benevolent culture of investment in people over equipment. Automation, however, can bestow multiple benefits through improvements in reproducibility, researcher efficiency, clinical translation, and safety. Less immediately obvious are the accompanying limitations, including obsolescence and an inhibitory effect on the freedom to innovate. Growing the range of automation options suitable for research laboratories will require more flexible, modular and cheaper designs. Academic and commercial developers of automation will increasingly need to design with an environmental awareness and an understanding that large high-tech robotic solutions may not be appropriate for laboratories with constrained financial and spatial resources. To fully exploit the potential of laboratory automation, future generations of scientists will require both engineering and biology skills. Automation in the research laboratory is likely to be an increasingly critical component of future research programs and will continue the trend of combining engineering and science expertise together to answer novel research questions.

Redundant meta-analyses are common in genetic epidemiology.

OBJECTIVES: The massive growth in the publication of meta-analyses may cause redundancy and wasted efforts. We performed a metaepidemiologic study to evaluate the extent of potential redundancy in published meta-analyses in genetic epidemiology. STUDY DESIGN AND SETTING: Using a sample of 38 index meta-analyses of genetic associations published in 2010, we retrieved additional meta-analyses that evaluated identical associations (same genetic variant and phenotype) using the Human Genome Epidemiology (HuGE) Navigator and PubMed databases. We analyzed the frequency of potential duplication and examined whether subsequent meta-analyses cited previous meta-analyses on the exact same association. RESULTS: Based on 38 index meta-analyses, we retrieved a total of 99 duplicate meta-analyses. Only 12 (32%) of the index meta-analyses were unambiguously unique. We found a mean of 2.6 duplicates and a median of 2 duplicates per meta-analysis. In case studies, only 29-54% of previously published meta-analyses were cited by subsequent ones. CONCLUSION: These results suggest that duplication is common in meta-analyses of genetic associations.

Patient burden and clinical advances associated with postapproval monotherapy cancer drug trials: a retrospective cohort study.

OBJECTIVES: After regulatory approval, drug companies, public funding agencies and academic researchers often pursue trials aimed at extending the uses of a new drug by testing it in new non-approved indications. Patient burden and clinical impact of such research are not well understood. DESIGN AND SETTING: We conducted a retrospective cohort study of postapproval clinical trials launched within 5 years after the drug's first approval, testing anticancer drugs in monotherapy in indications that were first pursued after a drug's first Food and Drug Administration (FDA) license, for all 12 anticancer drugs approved between 2005 and 2007. FDA, Medline and Embase search date 2019 February 12. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary objective was to measure burden and clinical impact for patients enrolling in these trials. Each trial was sorted into a 'trajectory' defined by the drug and cancer indication. The risk was operationalised by proportions of grade 3-4 severe adverse events and deaths. The clinical impact was measured by estimating the proportion of patients participating in trajectories that resulted in FDA approval, uptake into National Comprehensive Cancer Network (NCCN) clinical practice guidelines or advancement to randomised controlled trials within 8 years. RESULTS: Our search captured 104 published trials exploring monotherapy, including 69 unique trajectories. In total, trials in our sample enrolled 4699 patients. Grade 3-4 adverse events were experienced by 19.6% of patients; grade 5 events were experienced by 2.8% of patients. None of the trajectories launched after initial drug approval received FDA approval. Five trajectories were recommended by the NCCN within 8 years of the first trial within that trajectory. Eleven trajectories were advanced to randomised controlled testing. CONCLUSIONS: The challenges associated with unlocking new applications for drugs that first received approval from 2005 to 2007 were similar to those for developing new drugs altogether. Our findings can help inform priority setting in research and provide a basis for calibrating expectations when considering enrolment in label-extending trials.

Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study.

BACKGROUND: Using existing data from clinical registries to support clinical trials and other prospective studies has the potential to improve research efficiency. However, little has been reported about staff experiences and lessons learned from implementation of this method in pediatric cardiology. OBJECTIVES: We describe the process of using existing registry data in the Pediatric Heart Network Residual Lesion Score Study, report stakeholders' perspectives, and provide recommendations to guide future studies using this methodology. METHODS: The Residual Lesion Score Study, a 17-site prospective, observational study, piloted the use of existing local surgical registry data (collected for submission to the Society of Thoracic Surgeons-Congenital Heart Surgery Database) to supplement manual data collection. A survey regarding processes and perceptions was administered to study site and data coordinating center staff. RESULTS: Survey response rate was 98% (54/55). Overall, 57% perceived that using registry data saved research staff time in the current study, and 74% perceived that it would save time in future studies; 55% noted significant upfront time in developing a methodology for extracting registry data. Survey recommendations included simplifying data extraction processes and tailoring to the needs of the study, understanding registry characteristics to maximise data quality and security, and involving all stakeholders in design and implementation processes. CONCLUSIONS: Use of existing registry data was perceived to save time and promote efficiency. Consideration must be given to the upfront investment of time and resources needed. Ongoing efforts focussed on automating and centralising data management may aid in further optimising this methodology for future studies.

Evaluation of the scientific research efficiency of 100 tertiary hospitals and the influencing factors analysis based on Bootstrap DEA-OLS model / 中华医院管理杂志

Objective To evaluate the scientific research efficiency of tertiary hospitals and analyze the influencing factors, providing reference for the decision-making of scientific research managers in China. Methods Evaluation indicators were collected from 100 tertiary hospitals in China, and their research efficiency was evaluated using Bootstrap data envelopment analysis (DEA) method. The influencing factors were analyzed using ordinary least square ( OLS) regression model. Results The averaged scientific research efficiency of these hospitals using Bootstrap DEA method was 0. 5224, lower than that using conventional DEA method (0. 0676), yet with a great variation (from 0. 1103 to 0. 8790) among them. Linear regression analysis showed that factors such as R&D input and output, and hospital types has statistical significance on the saentific research efficiency (P < 0. 05). Conclusions These hospitals are inefficient in scientific research with sizable room for improvement. Conventional DEA method should be used with caution, as deviations should be corrected with Bootstrap DEA method. The research development level and hospital types of their province can influence the research efficiency the hospitals significantly.

The systematic review on the evaluation indictors for research efficiency on DEA model in research institutions in China / 中华医学科研管理杂志

Objective To systematically assess the application status of evaluation indicators of research efficiency in research institutions in China.Methods Literatures published before August 2016 were searched and retrieved from CNKI and Wanfang Database.Results 112 indicators used in 72 literatures were merged and analyzed.According to the usage frequency not less than 10％ and the top usage frequency in each indicator group,the main input indicators involved the number of total researchers and the total research funding.The output indicators mainly focused on the number of monographs,the number of academic papers,the number of patents granted,the number of research projects,the number of awards and the amount of money charged for technology transfer.Conelusions The indicators obtained from the previous research could provide a reference for the research efficiency evaluation of medical institutions in China.

Scientific Efficiency Evaluation of Tertiary Hospitals Based on Data Envelopment Analysis / 医学信息学杂志

Data Envelopment Analysis (DEA) method is used to evaluate the efficiency of 41 tertiary hospitals in 2015 in Beijing.The possible causes of non-DEA effective hospitals are analyzed,and the direction and measures of non-DEA effective hospitals to promote scientific research efficiency are put forward.

Establishing the role of honest broker: bridging the gap between protecting personal health data and clinical research efficiency.

Background. The objective of this study is to propose the four conditions for the roles of honest brokers through a review of literature published by ten institutions that are successfully utilizing honest brokers. Furthermore, the study aims to examine whether the Asan Medical Center's (AMC) honest brokers satisfy the four conditions, and examine the need to enhance their roles. Methods. We analyzed the roles, tasks, and types of honest brokers at 10 organizations by reviewing the literature. We also established a Task Force (TF) in our institution for setting the roles and processes of the honest broker system and the honest brokers. The findings of the literature search were compared with the existing systems at AMC-which introduced the honest broker system for the first time in Korea. Results. Only one organization employed an honest broker for validating anonymized clinical data and monitoring the anonymity verifications of the honest broker system. Six organizations complied with HIPAA privacy regulations, while four organizations did not disclose compliance. By comparing functions with those of the AMC, the following four main characteristics of honest brokers were determined: (1) de-identification of clinical data; (2) independence; (3) checking that the data are used only for purposes approved by the IRB; and (4) provision of de-identified data to researchers. These roles were then compared with those of honest brokers at the AMC. Discussion. First, guidelines that regulate the definitions, purposes, roles, and requirements for honest brokers are needed, since there are no currently existing regulations. Second, Korean clinical research institutions and national regulatory departments need to reach a consensus on a Korean version of Limited Data Sets (LDS), since there are no lists that describe the use of personal identification information. Lastly, satisfaction surveys on honest brokers by researchers are necessary to improve the quality of honest brokers.

Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research.

Society makes substantial investments in biomedical research, searching for ways to better human health. The product of this research is principally information published in scientific journals. Continued investment in science relies on society's confidence in the accuracy, honesty, and utility of research results. A recent focus on productivity has dominated the competitive evaluation of scientists, creating incentives to maximize publication numbers, citation counts, and publications in high-impact journals. Some studies have also suggested a decreasing quality in the published literature. The efficiency of society's investments in biomedical research, in terms of improved health outcomes, has not been studied. We show that biomedical research outcomes over the last five decades, as estimated by both life expectancy and New Molecular Entities approved by the Food and Drug Administration, have remained relatively constant despite rising resource inputs and scientific knowledge. Research investments by the National Institutes of Health over this time correlate with publication and author numbers but not with the numerical development of novel therapeutics. We consider several possibilities for the growing input-outcome disparity including the prior elimination of easier research questions, increasing specialization, overreliance on reductionism, a disproportionate emphasis on scientific outputs, and other negative pressures on the scientific enterprise. Monitoring the efficiency of research investments in producing positive societal outcomes may be a useful mechanism for weighing the efficacy of reforms to the scientific enterprise. Understanding the causes of the increasing input-outcome disparity in biomedical research may improve society's confidence in science and provide support for growing future research investments.

Evaluation on research efficiency of medical universities based on SE-DEA model / 中华医学科研管理杂志

Objective To evaluate the research input-output efficiency of 15 medical universities,and provide references for improving synthetic research efficiency.Methods Selected research input-output key indicators,and used CCR model and SE-DEA model to statistic and analyze.Results The average value of comprehensive efficiency of 15 medical universities was 0.811,pure technical efficiency value was 0.978,scale efficiency value was 0.827.There were 5 DEA efficient universities,7 pure technical efficient universities among 10 Non-DEA efficient universities.Conclusion The total research efficiency of medical universities in our country remained to improve,and should emphasize adjusting scientific research scale.

The organization of scientists and its relation to scientific productivity: Perceptions of Chinese stem cell researchers.

Chinese government funding of R&D ranks third in the world. Yet China ranks only 17th in terms of scientific productivity per unit of investment. The author recently conducted fieldwork on the team structure of 22 Chinese stem cell research groups. Interview data suggest that although Chinese research groups closely resemble their international counter-parts in many respects, there are also significant differences which are perceived by interviewees to affect levels of scientific productivity. One characteristic of Chinese research teams is a common deficiency in middle-layer positions. This shortage of experienced professionals is perceived by scientists participating in this study to have led to two consequences. First, inexperienced student researchers often form the backbone of scientific teams in China, which leads to frequent interruptions of research and extended laboratory training. Second, research teams consist of a relatively small number of personnel. These structural features are seen to create excessive social boundaries, which impede the exchange of information and further worsens the segmentation of resources. This article engages the question of the extent to which interviewees' local 'embedded' understandings of these difficulties may make a productive contribution to the analysis of the structural, and infra-structural, organization of Chinese professional bioscience teams.