Perspectives in Clinical Research最新文献_第8页

Evaluating large language models for selection of statistical test for research: A pilot study 评估大型语言模型，为研究选择统计测试：试点研究

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-08 DOI: 10.4103/picr.picr_275_23

Himel Mondal, Shaikat Mondal, Prabhat Mittal

In contemporary research, selecting the appropriate statistical test is a critical and often challenging step. The emergence of large language models (LLMs) has offered a promising avenue for automating this process, potentially enhancing the efficiency and accuracy of statistical test selection. This study aimed to assess the capability of freely available LLMs – OpenAI’s ChatGPT3.5, Google Bard, Microsoft Bing Chat, and Perplexity in recommending suitable statistical tests for research, comparing their recommendations with those made by human experts. A total of 27 case vignettes were prepared for common research models with a question asking suitable statistical tests. The cases were formulated from previously published literature and reviewed by a human expert for their accuracy of information. The LLMs were asked the question with the case vignettes and the process was repeated with paraphrased cases. The concordance (if exactly matching the answer key) and acceptance (when not exactly matching with answer key, but can be considered suitable) were evaluated between LLM’s recommendations and those of human experts. Among the 27 case vignettes, ChatGPT3.5-suggested statistical test had 85.19% concordance and 100% acceptance; Bard experiment had 77.78% concordance and 96.3% acceptance; Microsoft Bing Chat had 96.3% concordance and 100% acceptance; and Perplexity had 85.19% concordance and 100% acceptance. The intra-class correction coefficient of average measure among the responses of LLMs was 0.728 (95% confidence interval [CI]: 0.51–0.86), P < 0.0001. The test–retest reliability of ChatGPT was r = 0.71 (95% CI: 0.44–0.86), P < 0.0001, Bard was r = −0.22 (95% CI: −0.56–0.18), P = 0.26, Bing was r = −0.06 (95% CI: −0.44–0.33), P = 0.73, and Perplexity was r = 0.52 (95% CI: 0.16–0.75), P = 0.0059. The LLMs, namely, ChatGPT, Google Bard, Microsoft Bing, and Perplexity all showed >75% concordance in suggesting statistical tests for research case vignettes with all having acceptance of >95%. The LLMs had a moderate level of agreement among them. While not a complete replacement for human expertise, these models can serve as effective decision support systems, especially in scenarios where rapid test selection is essential.

在当代研究中，选择适当的统计检验是一个关键且往往具有挑战性的步骤。大型语言模型（LLM）的出现为这一过程的自动化提供了一个前景广阔的途径，有可能提高统计检验选择的效率和准确性。本研究旨在评估免费提供的 LLM（OpenAI 的 ChatGPT3.5、Google Bard、Microsoft Bing Chat 和 Perplexity）在为研究推荐合适的统计测试方面的能力，并将它们的建议与人类专家的建议进行比较。我们为常见的研究模型准备了共 27 个案例小故事，其中有一个问题是关于合适的统计测试。这些案例是根据以前发表的文献编制的，并由一名人类专家审查其信息的准确性。法学硕士们先用案例小故事提问，然后再用转述的案例重复这一过程。对法律硕士的建议与人类专家的建议之间的一致性（如果完全符合答案要点）和可接受性（如果不完全符合答案要点，但可被视为合适）进行评估。在 27 个案例小节中，ChatGPT3.5 建议统计测试的吻合度为 85.19%，接受度为 100%；Bard 实验的吻合度为 77.78%，接受度为 96.3%；Microsoft Bing Chat 的吻合度为 96.3%，接受度为 100%；Perplexity 的吻合度为 85.19%，接受度为 100%。本地语言学者的平均测量值的类内校正系数为 0.728（95% 置信区间 [CI]：0.51-0.86），P < 0.0001。ChatGPT 的测试-再测信度为 r = 0.71（95% CI：0.44-0.86），P < 0.0001；Bard 的测试-再测信度为 r = -0.22（95% CI：-0.56-0.18），P = 0.26；Bing 的测试-再测信度为 r = -0.06（95% CI：-0.44-0.33），P = 0.73；Perplexity 的测试-再测信度为 r = 0.52（95% CI：0.16-0.75），P = 0.0059。在建议对研究案例小节进行统计测试时，LLMs（即 ChatGPT、Google Bard、Microsoft Bing 和 Perplexity）的一致性均大于 75%，接受度均大于 95%。LLM 之间的一致性处于中等水平。这些模型虽然不能完全取代人类的专业知识，但可以作为有效的决策支持系统，尤其是在需要快速选择测试的情况下。

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引用次数: 0

Ethics committee accreditation: Journey from voluntariness to essentiality for quality sustenance 伦理委员会认证：从自愿到质量保障的必备条件

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 DOI: 10.4103/picr.picr_45_24

R. Tripathi

引用次数: 0

Effectiveness and safety of regimen containing bedaquiline and delamanid in patients with drug-resistant tuberculosis. 对耐药性结核病患者采用含有贝达喹啉和地拉那米的治疗方案的有效性和安全性。

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 Epub Date: 2023-09-11 DOI: 10.4103/picr.picr_1_23

Oki Nugraha Putra, Yulistiani Yulistiani, Soedarsono Soedarsono, Susi Subay

Background: Bedaquiline and delamanid have been included in the individualized treatment regimen (ITR) to treat patients with drug-resistant tuberculosis (DR-TB).

Objective: The objective of this study is to compare the effectiveness of sputum culture conversion and the safety of ITR containing bedaquiline and delamanid.

Methods: Data were collected retrospectively from medical records of DR-TB patients who received ITR between January 2020 and December 2021. Patients were divided into bedaquiline and bedaquiline-delamanid groups. Sputum culture was evaluated until 6 months of treatment. Measurement of QTc interval, renal and liver function test, and serum potassium were evaluated to assess safety during the study period. We used Chi-square to analyze a difference in cumulative culture conversion; meanwhile, Wilcoxon and Mann-Whitney tests were used to analyze differences in laboratory data for each and between the two groups, respectively.

Results: Fifty-one eligible DR-TB patients met the inclusion criteria, 41 in the bedaquiline and 10 in bedaquiline-delamanid group. 43/51 patients had a positive culture at baseline. After 6 months of treatment, 42/43 DR-TB patients (97.6%) had sputum culture conversion and no difference between the two groups (P ≥ 0.05). QTc interval within normal limit and no patient had a QTc >500 ms during the study period. Creatinine levels significantly differed between the two groups 6 months after treatment (P < 0.05).

Conclusion: DR-TB patients who received all oral ITR containing bedaquiline and or delamanid demonstrated favorable sputum conversion with a tolerable safety profile.

背景：贝达喹啉（Bedaquiline）和德拉马尼（delamanid）已被纳入个体化治疗方案（ITR），用于治疗耐药结核病（DR-TB）患者：贝达喹啉和德拉马尼已被纳入个体化治疗方案（ITR），用于治疗耐药结核病（DR-TB）患者：本研究旨在比较含有贝达喹啉和地拉马尼的个体化治疗方案的痰培养转换效果和安全性：从2020年1月至2021年12月期间接受ITR治疗的DR-TB患者的病历中回顾性收集数据。患者被分为贝达喹啉组和贝达喹啉-德拉马尼组。对治疗 6 个月前的痰培养进行评估。为评估研究期间的安全性，还对 QTc 间期、肝肾功能检测和血清钾进行了评估。我们使用Chi-square来分析累计培养转换率的差异；同时，使用Wilcoxon和Mann-Whitney检验分别分析每组和两组之间实验室数据的差异：51例符合纳入标准的DR-TB患者中，41例为贝达喹啉组，10例为贝达喹啉-地拉那米德组。43/51 名患者的基线培养结果呈阳性。治疗 6 个月后，42/43 例 DR-TB 患者（97.6%）痰培养转阴，两组间无差异（P≥0.05）。研究期间，QTc 间期在正常范围内，没有患者 QTc >500 ms。两组患者在治疗 6 个月后的肌酐水平有明显差异（P < 0.05）：结论：接受含有贝达喹啉和或地拉马尼的所有口服 ITR 的 DR-TB 患者的痰液转阴情况良好，且安全性可耐受。

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引用次数: 0

Impact of accreditation on registered ethics committees in terms of quality and governance in India: A cross-sectional study 认证对印度注册伦理委员会质量和管理的影响：横向研究

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 DOI: 10.4103/picr.picr_153_23

G. Dakhale, M. Kalikar, A. Giradkar

Ethics Committee accreditation is a process to assess the performance against a set of standards. Very few studies have shown that process of accreditation results in the improvement of the overall functioning of ECs. in terms of quality and governance. Hence, the present study was planned to evaluate the impact of accreditation on registered EC in terms of quality and governance and to compare functioning of accredited versus non accredited EC in terms of quality and governance. This was a cross sectional, observational, questionnaire-based survey conducted on 28 registered Ethics Committee in India after approval from the Institutional Ethics Committee. Accredited EC’s (n = 12) were compared for NABH standard for accreditation before and after accreditation in terms of percentage. It was found that majority of the standards related to structure and composition, adherence to specific policies , completeness of review and after approval process were met by majority of EC’s after accreditation. Only a few EC ‘s fulfilled some of the criteria before accreditation. There was a statistically significant difference with reference to adherence to specific policies by accredited and non-accredited EC’s like updating SOP according to changing requirements (P < 0.0237), process for preparing SOP (P < 0.0237), categorization of review process mentioned in SOP (P < 0.0237) procedure to be followed for vulnerable population (P < 0.0103) , process of handling issues related to complaints by participants and other stakeholders violation (P < 0.0103) etc. Accreditation results in improving of EC functioning in terms of quality and governance.

道德操守委员会认证是根据一套标准评估其绩效的过程。很少有研究表明，认证过程能改善伦理委员会在质量和管理方面的整体运作。因此，本研究计划从质量和管理方面评估资格认证对注册选委会的影响，并从质量和管理方面比较通过资格认证和未通过资格认证的选委会的运作情况。这是一项横断面、观察性、问卷调查，在获得机构伦理委员会批准后，对印度 28 个注册伦理委员会进行了调查。对获得认证的伦理委员会（n = 12）在获得认证前后的 NABH 认证标准进行了百分比比较。结果发现，大多数伦理委员会在通过鉴定后都达到了与结构和组成、遵守特定政策、审查完整性和批准后流程有关的标准。只有少数教委在评审前达到了部分标准。经评审和未经评审的教委在遵守具体政策方面存在显著差异，如根据不断变化的要求更新标准操作程序（P < 0.0237）、编写标准操作程序的过程（P < 0.0237）、标准操作程序中提及的审查过程分类（P < 0.0237）、弱势人群应遵循的程序（P < 0.0103）、处理与参与者和其他利益相关者违规投诉有关的问题的过程（P < 0.0103）等。评审结果改善了教委在质量和管理方面的运作。

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引用次数: 1

A descriptive study of new drug approvals during 2017-2021 and disease morbidity and mortality patterns in India. 印度 2017-2021 年新药批准情况及疾病发病率和死亡率模式的描述性研究。

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 Epub Date: 2023-09-25 DOI: 10.4103/picr.picr_109_23

Urvashi Gupta, Ashwin Kamath, Priyanka Kamath

Aim: Studies show the presence of a mismatch between drug research and disease burden. A study conducted in the European Union found that new drug development was restricted to certain diseases. A study of biosimilar approvals in India found that 87% of drugs were for treating noncommunicable diseases. This study aimed to determine the new drugs approved in India from 2017 to 2021 and the top ten causes of morbidity and mortality and detect the presence of any discordance between these.

Methods: A descriptive study was conducted using data on new drug approvals accessed from the Central Drugs Standard Control Organization website. The top ten causes of mortality and morbidity in India from 2015 to 2019 were identified from the Global Burden of Diseases database. Descriptive statistics were used to compare the drug approvals and the leading diseases.

Results: One hundred twenty-six drugs were approved during the study period. Antineoplastic drugs constituted 19.84% of the approvals, antimicrobials 18.25%, and cardiovascular drugs 9.52%. Ischemic heart disease and chronic obstructive pulmonary disease were the two leading causes of morbidity and mortality. Diarrheal diseases, lower respiratory tract infection, and drug-susceptible tuberculosis were among the top ten causes. Ten antibacterials, including four antitubercular drugs, were approved during this period. Two drugs were approved for rare diseases.

Conclusion: Our study showed that the drugs approved were largely in line with the prevalent disease burden, and there was no significant discordance observed. Some diseases, such as ischemic stroke/intracranial hemorrhage, require further efforts in bringing forth newer pharmacotherapy options.

目的：研究表明，药物研究与疾病负担之间存在不匹配。在欧盟进行的一项研究发现，新药开发仅限于某些疾病。对印度生物仿制药批准情况的研究发现，87%的药物用于治疗非传染性疾病。本研究旨在确定2017年至2021年印度批准的新药以及十大发病和死亡原因，并检测这两者之间是否存在任何不一致：利用从中央药品标准控制组织网站获取的新药批准数据进行了描述性研究。从全球疾病负担数据库中确定了 2015 年至 2019 年印度十大死亡和发病原因。使用描述性统计来比较药物批准情况和主要疾病：研究期间批准了 126 种药物。抗肿瘤药物占获批药物的 19.84%，抗菌药物占 18.25%，心血管药物占 9.52%。缺血性心脏病和慢性阻塞性肺病是发病和死亡的两大主要原因。腹泻病、下呼吸道感染和药物敏感性结核病位列十大病因。在此期间，包括四种抗结核药物在内的十种抗菌药物获得批准。有两种药物被批准用于治疗罕见疾病：我们的研究表明，批准的药物与流行病负担基本一致，没有发现明显的不一致。一些疾病，如缺血性中风/颅内出血，需要进一步努力推出更新的药物治疗方案。

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引用次数: 0

Handling missing data in research 处理研究中的缺失数据

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 DOI: 10.4103/picr.picr_38_24

P. Ranganathan, Sally Hunsberger

Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in which missing data can be minimized.

缺失数据是研究工作中不可避免的一部分，它会导致可分析人口数量的减少以及估计值的偏差和不精确。本文将讨论缺失数据的类型、处理缺失数据的方法，并提出尽量减少缺失数据的方法。

引用次数: 0

Impact of education on knowledge and attitude related to pharmacovigilance and reporting of adverse drug reactions among community pharmacists in Yemen: A pre- and postinterventional study 教育对也门社区药剂师药物警戒和报告药物不良反应相关知识和态度的影响：干预前后研究

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-04-01 DOI: 10.4103/picr.picr_160_23

Khalifah Abdulwahid, Nur Aizati Athirah Daud, Y. Al-Worafi, Mohamed Azmi Ahmad Hassali

引用次数: 0

Artificial intelligence in pharmacovigilance – Opportunities and challenges 人工智能在药物警戒领域的应用--机遇与挑战

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-03-27 DOI: 10.4103/picr.picr_290_23

Mira Kirankumar Desai

Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating “needles from haystack,” is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects – people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.

药物警戒（PV）是一个以数据为驱动的过程，通过处理可疑不良事件（AE）报告和提取健康数据，尽早发现药品安全问题。药物警戒案例处理周期始于数据收集、数据录入、初步检查完整性和有效性、编码、因果关系、预期性、严重性和严重程度的医学评估、随后提交报告、质量检查以及数据存储和维护。这需要大量人力和专业技术知识，因此既昂贵又耗时。由于个人病例安全报告的智能收集和报告，PV 数据库中疑似 AE 报告的数量呈指数级增长，医疗保健专业人员和患者的意识和参与程度提高，从而扩大了基础。对于制药公司、监管机构、医疗和 PV 专家以及国家药物警戒项目管理人员等主要利益相关者来说，如何处理数量庞大、种类繁多的数据、合理利用这些数据并将其从 "大海捞针 "中分离出来是一项挑战。医疗保健领域的人工智能（AI）在严重依赖医学影像解读的专业领域表现抢眼。同样，人们对采用人工智能工具来补充和自动化 PV 流程的兴趣也在不断增长。先进的技术当然可以补充病例处理过程中的常规、重复和人工任务，并提高效率；然而，在整个病例处理生命周期中实施这种技术并产生实际影响，却提出了一些问题和挑战。光伏系统的完全自动化是一把双刃剑，需要考虑两个方面--人员和流程。重点应该是将专业技术（人员）与智能技术（流程）相结合的协作方法，以增强人的才能，从而实现光伏系统的目标，并使所有利益相关者受益。人工智能技术应增强人类智慧，而不是取代人类专家。重要的是要强调并确保人工智能能为光伏行业带来更多益处，而不是挑战。本综述介绍了印度医疗保健系统的优势和突出的科学、技术和政策问题，以及人工智能工具在实现完全自动化方面的成熟度。

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引用次数: 0

Exploring the bioethical implications of using artificial intelligence in writing research proposals 探索使用人工智能撰写研究提案的生物伦理影响

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-02-26 DOI: 10.4103/picr.picr_226_23

S. Shivananda, V. Doddawad, C. S. Vidya, J. Chandrakala

Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.

人工智能（AI）在协助研究人员撰写研究计划书方面有着巨大的潜力，它可以生成假设、识别文献并提出数据收集和分析的方法。然而，在撰写研究计划书时使用人工智能会产生重要的生物伦理影响，包括无意中传播偏见，以及对人类专业知识和判断在研究过程中的作用提出质疑。本文探讨了在研究计划书撰写中使用人工智能的伦理影响，并提出了在此背景下负责任地、合乎伦理地使用人工智能的指导原则。本文将回顾在撰写研究计划书时使用人工智能可能带来的益处和挑战，讨论人类专业知识和判断力的作用，并提出在开发和使用人工智能系统时提高透明度和问责制的指导方针。最终，要解决研究计划书撰写中与人工智能相关的生物伦理问题，需要利益相关者之间持续不断的对话与合作，以及对透明度、问责制和伦理原则的承诺。

引用次数: 0

Procalcitonin-guided antimicrobial stewardship in critically ill patients with sepsis: A pre– post interventional study 脓毒症重症患者在降钙素原指导下的抗菌药物管理：干预前干预后研究

Q2 Medicine

Perspectives in Clinical Research

Pub Date : 2024-02-26 DOI: 10.4103/picr.picr_298_23

Philip Mathew, S. Vargese, Litha Mary Mathew, Alice David, J. Saji, Ann Mariam Varghese

Injudicious usage of antibiotics has led to the emergence of antibiotic resistance which is a major health-care problem in developing countries such as India. Our aim was to show how antibiotic therapy based on serial procalcitonin (PCT) assay can help in antibiotic de-escalation in septic patients. A pre–post interventional study was conducted among 300 septic patients admitted to an intensive care unit (ICU). All septic patients admitted 2 months before and 2 months after the introduction of monitoring of PCT were included and they were divided into Group P (with PCT monitoring) and Group C (without PCT monitoring). The proportion of patients for whom antimicrobials were de-escalated, the average time taken to de-escalate antimicrobials, and the average duration of ICU stay were compared. Proportions and averages with standard deviations were calculated to describe the data. A test of proportions was done to compare the proportion de-escalated and a Student’s t-test was done to compare the average duration of antibiotic therapy. The proportion of patients in whom de-escalation of antimicrobials was done was 125 (83.33%) in Group P as compared to 92 (61.33%) in Group C. The time taken to de-escalate was 3.04 ± 0.83 days (95% confidence interval [CI] 2.89–3.18) in Group P compared to 4.7 ± 1.4 days (CI 4.41–4.98) in Group C. The duration of ICU stay was also less in Group P - 3.08 ± 0.91 days (CI 3.08–3.38) as compared to Group C - 5.16 ± 2.17 days (4.80–5.51). Serial PCT assay-based antimicrobial therapy helped to wean patients with sepsis off antimicrobials earlier thus reducing the duration of ICU stay.

抗生素的滥用导致了抗生素耐药性的出现，这是印度等发展中国家的一个主要医疗保健问题。我们的目的是说明基于系列降钙素原 (PCT) 检测的抗生素疗法如何帮助脓毒症患者降低抗生素耐药性。我们在重症监护室（ICU）收治的 300 名脓毒症患者中开展了一项干预前-干预后研究。所有在引入 PCT 监测前 2 个月和引入 PCT 监测后 2 个月入院的脓毒症患者都被纳入其中，并被分为 P 组（有 PCT 监测）和 C 组（无 PCT 监测）。比较了不再使用抗菌药物的患者比例、不再使用抗菌药物所需的平均时间以及重症监护病房的平均住院时间。通过计算比例、平均值和标准差来描述数据。采用比例检验比较停用抗菌药物的比例，采用学生 t 检验比较抗生素治疗的平均持续时间。 P组患者中使用抗菌药物的比例为125人（83.33%），C组为92人（61.33%）。P 组的重症监护室住院时间为 3.08 ± 0.91 天（CI 3.08-3.38），而 C 组为 5.16 ± 2.17 天（4.80-5.51）。基于 PCT 检测的系列抗菌疗法有助于脓毒症患者尽早停用抗菌药物，从而缩短重症监护室的住院时间。

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引用次数: 0