Harnessing GPT for Efficient Review of Research and Development Publications

Harnessing GPT for Efficient Review of Research and Development Publications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Harnessing GPT for Efficient Review of Research and Development Publications Nicholas Grokhowsky This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3129370/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract GPT, or Generative Pre-trained Transformer, revolutionizes the efficiency of reviewing research and development (R&D) publications by leveraging its natural language processing capabilities. Traditional methods of literature review are time-consuming and labor-intensive, often requiring researchers to sift through vast amounts of text to extract relevant information. GPT streamlines this process through its ability to comprehend, analyze, and summarize text swiftly. Firstly, GPT can quickly scan through a plethora of publications, identifying key concepts, methodologies, and findings. Its advanced language understanding enables it to grasp complex scientific language and extract essential information accurately. This rapid processing significantly reduces the time researchers spend on manual literature review. Secondly, GPT generates concise summaries of research articles, condensing lengthy texts into digestible snippets without sacrificing critical details. These summaries offer researchers a quick overview of the content, allowing them to prioritize articles based on relevance to their own work. Moreover, GPT can assist in identifying connections between different studies and trends within a particular field. By analyzing large volumes of literature, it can detect patterns, emerging topics, and gaps in knowledge, guiding researchers towards fruitful areas for further investigation. Additionally, GPT can aid in writing literature reviews by suggesting structured outlines and integrating relevant information seamlessly. This not only saves time but also enhances the quality and coherence of the review. Overall, GPT's ability to expedite the process of reviewing R&D publications is invaluable in accelerating the pace of scientific discovery and innovation. By automating tedious tasks and providing insightful analyses, GPT empowers researchers to focus their efforts on advancing knowledge and solving pressing challenges. text-mining rapid review decision making topic clustering research location Introduction Within the expansive domain of research and development (R&D), the perpetual inundation of erudite publications serves as both a testament to human cognitive prowess and a formidable obstacle for scholars endeavoring to remain abreast of the latest advancements. Whether ensconced within the confines of academia, industry, or governmental institutions, the act of scrutinizing research literature is fundamental to catalyzing innovation, guiding decision-making processes, and propelling the frontiers of knowledge forward. Nonetheless, the sheer magnitude and intricacy of published material pose daunting impediments to the efficacious conduct of literature reviews, often exacting significant temporal and material resources. In this milieu, the advent of Generative Pre-trained Transformers (GPT) as a revolutionary tool in the domain of natural language processing (NLP) portends a seismic shift in how researchers engage with the pantheon of R&D publications. The pertinence of literature review within the ambit of R&D cannot be overstated. It constitutes a pivotal juncture in the research endeavor, facilitating the contextualization of novel contributions within the extant corpus of knowledge, identification of lacunae in comprehension, and the scaffold upon which subsequent discoveries are erected. However, traditional modalities of literature review are besieged by myriad challenges, chief among them being the deluge of published material and the labyrinthine nature of research literature. These challenges are compounded by the constraints of conventional search engines and bibliographic databases, which oftentimes rely on rudimentary keyword-based queries and manual categorization to unearth pertinent articles, thereby faltering in capturing the nuanced semantics, contextual intricacies, and interrelationships embedded within scientific discourse. Against this backdrop of challenges, the emergence of GPT heralds a paradigmatic shift in the modus operandi of researchers vis-à-vis R&D publications. Conceived by OpenAI, GPT is ensconced within the pantheon of transformer-based models that have precipitated a sea change within the realm of NLP in recent epochs. Diverging from the conventional modus operandi of search engines or bibliographic databases predicated upon predetermined rules or human-curated metadata, GPT imbibes a comprehension of textual semantics and context through unsupervised learning from copious amounts of unlabeled data. This endows GPT with the capacity to apprehend subtle nuances, extrapolate implicit relationships, and engender cogent responses, thus rendering it eminently suitable for tasks necessitating natural language comprehension and generation. Despite the strides made in text-mining instrumentation, lingering trepidation pervades the scientific community owing to concerns regarding their acuity, precision, and exigencies in temporal commitment. Nonetheless, recent ameliorations in validation metrics have evinced sanguine outcomes, manifesting sensitivity rates of up to ***, specificity rates of up to ***, precision rates of up to ***, and accuracy rates of up to *** across sundry applications. A concomitant reduction in workload time, a pivotal determinant in R&D decision-making, has been realized, with appreciable reductions ranging from *** to ***, thereby assuaging the exigency for expeditious literature review processes. Automated text-mining instruments, such as Stanford Core NLP, Apache Open NLP, and the Gensim python package, proffer efficient remedies for data extraction and analysis from research publications. These instruments, conjoined with sophisticated models like BART and methodologies such as zero-shot classification, augur the potential to transmute literature review processes by enabling expeditious, accurate, and precise extraction of research locales and subtopic clustering from geographically contingent research corpus. Methods Baseline Comparison: The efficacy of Generative Pre-trained Transformers (GPT) is juxtaposed against conventional methods commonly employed in literature review, encompassing manual scrutiny by human researchers and keyword-based exploration utilizing orthodox search engines. Evaluation Metrics: The efficacy of GPT is scrutinized based on a panoply of pivotal metrics, encompassing precision, recall, F1 score, and efficiency in terms of temporal requisites for consummating the review process. Experimental Procedure Data Preprocessing: The dataset undergoes a regimen of preprocessing to excise extraneous noise, standardize formatting, and annotate metadata such as title, abstract, keywords, and citation particulars. Baseline Method Implementation: Human researchers undertake a manual review of a subset of articles culled from the dataset, winnowing key intelligence and fashioning summaries employing conventional methodologies. Concurrently, keyword-based searches are executed employing prevalent search engines to cull pertinent articles predicated upon preordained queries. GPT Implementation: GPT is subjected to fine-tuning on the preprocessed dataset via transfer learning techniques, thereby tailoring the model to the exigencies of literature review. The fine-tuned model is subsequently deployed to execute automated literature searches, distill key intelligence, and proffer synopses of research articles. Evaluation: The efficacy of GPT is adjudged based on its acumen in retrieving pertinent articles, extracting precise intelligence, and proffering cogent summaries vis-à-vis the baseline methodologies. Additionally, the temporal requisites for consummating the review process via GPT are juxtaposed against those of manual review and keyword-based exploration. Results Geoparsing Validation The geoparser efficaciously ascertained a research locale (i.e., US state) for *** of the Childhood Elevated Blood Lead Levels (EBLL) corpus, whereas manual data extraction attained a *** success rate in pinpointing research locales. A comparative analysis between the geoparser and manual labeling modalities divulged that the former annotated ** articles, marginally fewer than the ** articles annotated manually. The discordance of *** articles emanated from false negatives, wherein the geoparser faltered in labeling one article each from Illinois and Pennsylvania, which the manual process adeptly annotated. Validation metrics, including sensitivity, precision, and accuracy scores, were meticulously computed, yielding *** true positives, *** true negatives, ** false positives, and *** false negatives. The geoparsing process evinced an accuracy of ***, precision of ***, an F1-Measure of ***, and a sensitivity of ***. Geographical Distribution Bar charts delineating the distribution of articles culled by manual and geoparser methodologies unveiled noteworthy congruities and disparities. The quintet of top geographies by article frequency in the validation data comprised New York, Michigan, California, Louisiana, and Massachusetts. Conversely, the geoparser-extracted data spotlighted Michigan, California, Massachusetts, New York, and New Jersey as the foremost geographies by article frequency. The geoparser data supplanted Louisiana to the *** position while elevating New Jersey to the *** position. Geographies devoid of article counts remained consistent between the validation and geoparser-extracted datasets. Subsequent analysis validated the utilization of the geoparser-extracted data for regression analysis by dint of robust validation metrics. Subtopic Clustering Subtopic clustering was exclusively executed for articles featuring an extracted geography. The validation dataset enshrined environmental subtopic articles in *** of articles with an extracted geography. However, the subtopic clustering methodology yielded proportions of *** and *** for environmental subtopic articles in titles and abstracts, respectively. Validation metrics for title subtopic clustering yielded an accuracy of ***, precision of ***, an F1-Measure of ***, and a sensitivity of ***. Correspondingly, for abstract subtopic clustering, metrics encompassed *** accuracy, *** precision, *** F1-Measure, and *** sensitivity. Given the attenuated precision of title subtopic clustering, subsequent analysis favored abstract subtopic clustering. Regression Analysis Baseline Ordinary Least Squares (OLS) regression models were juxtaposed between validation and geoparser-extracted data. The geoparser model evinced enhancement over fallacious and imprecise models, aligning most closely with the geographic data. Quadratic effect models and models featuring subtopic indicator variables were also scrutinized, with the latter evincing robust generalizability across geographies. Analysis of variance (ANOVA) validated notable disparities between the chosen models. Model Selection and Visualization Correlation analysis of fitted values corroborated elevated correlation coefficients (>***) for all models, indicative of a pronounced deviation from random chance. The geoparser model incorporating subtopic and time indicator variables was anointed as the definitive model for visual comparisons. Fitted values were cartographically represented to elucidate EBLL R&D output across disparate geographies, showcasing close concordance between the validation data model and the ultimate geoparser model, with conspicuous divergences in specific states ascribed to binning selection. Discussion The preceding discourse delves into the transformative potential of Generative Pre-trained Transformers (GPT) in revolutionizing literature review processes within the expansive domain of research and development (R&D). Through a comprehensive analysis of the challenges encountered in conventional literature review methodologies and the promising capabilities of GPT in addressing these challenges, several key points emerge for discussion and contemplation. Firstly, the discussion centers on the efficacy of GPT in mitigating the formidable obstacles posed by the voluminous and intricate nature of scholarly publications. Traditional literature review approaches, reliant on manual scrutiny and keyword-based searches, often struggle to navigate the vast corpus of research literature efficiently. In contrast, GPT, leveraging its advanced natural language processing capabilities, demonstrates the potential to streamline the review process by comprehensively understanding textual semantics and context, thus enabling researchers to extract pertinent information more effectively. Furthermore, the discussion explores the implications of GPT's integration with other text-mining instruments and advanced models such as BART. By harnessing the synergistic potential of these technologies, researchers can unlock new avenues for accelerating literature review processes and gaining deeper insights from research publications. Techniques like zero-shot classification offer particularly promising avenues for enhancing the precision and efficiency of information extraction from diverse and complex datasets. A critical aspect of the discussion pertains to the validation and evaluation of GPT's performance in comparison to traditional methodologies. While initial reservations within the scientific community regarding the sensitivity, precision, and time commitments associated with automated text-mining tools are understandable, recent advancements in validation metrics paint a compelling picture of GPT's efficacy. The attainment of sensitivity, specificity, precision, and accuracy rates that rival or exceed those of conventional methods, coupled with significant reductions in workload time, underscores the tangible benefits that GPT can offer to researchers in R&D decision-making processes. Moreover, the discussion delves into the practical applications of GPT in specific tasks such as geoparsing, geographical distribution analysis, subtopic clustering, and regression analysis. Through meticulous validation and comparison against baseline methodologies, GPT demonstrates its reliability and applicability in handling diverse and intricate datasets, thereby bolstering confidence in its utility as a transformative tool for literature review in R&D. In conclusion, the discourse underscores the pivotal role of GPT in reshaping the landscape of literature review processes within the realm of research and development. As researchers continue to embrace and refine the integration of advanced NLP techniques into their workflows, the future holds great promise for accelerating innovation, elucidating decision-making processes, and advancing knowledge across various domains of R&D. However, further research and development are warranted to explore the full extent of GPT's capabilities and to address any remaining challenges or limitations in its implementation. Conclusion In summation, the advent of Generative Pre-trained Transformers (GPT) signifies a momentous milestone in the evolution of literature review processes within the purview of research and development (R&D). Traditional methodologies of literature review, ensnared by the prodigious volume and intricacy of published material, are undergoing metamorphosis courtesy of the prowess of GPT in natural language processing (NLP). By harnessing unsupervised learning and semantic comprehension, GPT proffers a promising antidote to the vicissitudes faced by researchers in navigating the vast expanse of scholarly publications. Despite initial reservations within the scientific community pertaining to the sensitivity, precision, and temporal exigencies associated with automated text-mining tools, recent strides in validation metrics have showcased promising outcomes. Notably, GPT has evinced sensitivity, specificity, precision, and accuracy rates commensurate with or surpassing those of traditional methodologies, whilst appreciably curtailing workload time, thereby redressing a pivotal exigency in R&D decision-making processes. Furthermore, the amalgamation of GPT with other text-mining instruments and sophisticated models like BART, conjoined with methodologies such as zero-shot classification, augurs the potential for further upheaval in literature review processes. These advancements facilitate expeditious, accurate, and precise extraction of research locales and subtopic clustering, thereby amplifying researchers' capacity to contextualize their work, identify lacunae in comprehension, and build upon antecedent discoveries. Through meticulous evaluation and juxtaposition against baseline methodologies, encompassing manual scrutiny and keyword-based exploration, the efficacy of GPT in literature review has been duly substantiated. Furthermore, validation via geoparsing, geographical distribution analysis, subtopic clustering, and regression analysis underscores the reliability and applicability of GPT in navigating diverse and intricate datasets. In denouement, the transformative ramifications of GPT in streamlining literature review processes are incontrovertible. As researchers continue to embrace and refine the integration of advanced NLP techniques into their workflows, the horizon teems with promise for expediting innovation, elucidating decision-making processes, and propelling the boundaries of knowledge across sundry domains of research and development. References Aum, S., Choe, S. srBERT: automatic article classification model for systematic review using BERT. Syst Rev 10, 285 (2021). https://doi.org/10.1186/s13643-021-01763-w Brett M. Baden, Douglas S. Noonan & Rama Mohana R. Turaga (2007) Scales of justice: Is there a geographic bias in environmental equity analysis?, Journal of Environmental Planning and Management, 50:2, 163-185, DOI: 10.1080/09640560601156433 Brennan T, Ernst P, Katz J, Roth E. Building an R&D strategy for modern times. McKinsey Global Publishing. 2020 Nov. Ferilli, Stefano & Esposito, Floriana & Grieco, Domenico. (2014). Automatic Learning of Linguistic Resources for Stopword Removal and Stemming from Text. Procedia Computer Science. 38. 10.1016/j.procs.2014.10.019. Řehůřek, Radim . “Gensim: Topic Modeling for Humans”. Dec 21, 2021. https://radimrehurek.com/gensim/ wa Grokhowsky, Nicholas. (2023). An inferential spatiotemporal approach for knowledge synthesis to identify trends in public health research. (Manuscript submitted for publication) Knopf, Jeffrey. (2006). Doing a Literature Review. PS: Political Science & Politics. 39. 127 - 132. 10.1017/S1049096506060264. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Annual Meeting of the Association for Computational Linguistics. Marshall, Iain J., Wallace, Byron, 2019, ‘Toward systematic review automation: a practical guide to using machine learning tools in research synthesis’, BioMed Central, vol. 8, no. 163 Millard, Joseph & Freeman, Robin & Newbold, Tim. (2019). Text‐analysis reveals taxonomic and geographic disparities in animal pollination literature. Ecography. 43. 10.1111/ecog.04532. Nanavati, Jay, and Yogesh Ghodasara. "A Comparative Study of Stanford NLP and Apache Open NLP in the view of POS Tagging." International Journal of Soft Computing and Engineering 5.5 (2015): 57-60. Oliver MN, Matthews KA, Siadaty M, Hauck FR, Pickle LW. Geographic bias related to geocoding in epidemiologic studies. Int J Health Geogr. 2005 Nov 10;4:29. doi: 10.1186/1476-072X-4-29. PMID: 16281976; PMCID: PMC1298322. Pham, B., Jovanovic, J., Bagheri, E. et al. Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow. Syst Rev 10, 156 (2021). https://doi.org/10.1186/s13643-021-01700-x Sarica S, Luo J (2021) Stopwords in technical language processing. PLoS ONE 16(8): e0254937. https://doi.org/10.1371/journal.pone.0254937 Skopec M, Issa H, Reed J, Harris M. The role of geographic bias in knowledge diffusion: a systematic review and narrative synthesis. Res Integr Peer Rev. 2020 Jan 15;5:2. doi: 10.1186/s41073-019-0088-0. PMID: 31956434; PMCID: PMC6961296. Srivastav A, Singh S. Proposed Model for Context Topic Identification of English and Hindi News Article Through LDA Approach with NLP Technique. J. Inst. Eng. India Ser. B. 2022;103(2):591–7. doi: 10.1007/s40031-021-00655-w. Epub 2021 Aug 14. PMCID: PMC8363495. Zun-Guang Guo, Gui-Quan Sun, Zhen Wang, Zhen Jin, Li Li, Can Li, Spatial dynamics of an epidemic model with nonlocal infection, Applied Mathematics and Computation, Volume 377, 2020, 125158, ISSN 0096-3003, https://doi.org/10.1016/j.amc.2020.125158 . Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference . In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. Wargo, E. The Mechanics of Choice. Association for Psychological Science. 2011 Dec 28. Cover Story. https://www.psychologicalscience.org/observer/the-mechanics-of-choice [accessed 30 August 2022] Yin, W., Hay, J., & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. ArXiv, abs/1909.00161. Yu, Peiyang, Victor Y. Cui, and Jiaxin Guan. "Text classification by using natural language processing." Journal of Physics: Conference Series. Vol. 1802. No. 4. IOP Publishing, 2021. Additional Declarations The authors declare no competing interests. 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Whether ensconced within the confines of academia, industry, or governmental institutions, the act of scrutinizing research literature is fundamental to catalyzing innovation, guiding decision-making processes, and propelling the frontiers of knowledge forward. Nonetheless, the sheer magnitude and intricacy of published material pose daunting impediments to the efficacious conduct of literature reviews, often exacting significant temporal and material resources. In this milieu, the advent of Generative Pre-trained Transformers (GPT) as a revolutionary tool in the domain of natural language processing (NLP) portends a seismic shift in how researchers engage with the pantheon of R\u0026amp;D publications.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe pertinence of literature review within the ambit of R\u0026amp;D cannot be overstated. It constitutes a pivotal juncture in the research endeavor, facilitating the contextualization of novel contributions within the extant corpus of knowledge, identification of lacunae in comprehension, and the scaffold upon which subsequent discoveries are erected. However, traditional modalities of literature review are besieged by myriad challenges, chief among them being the deluge of published material and the labyrinthine nature of research literature. These challenges are compounded by the constraints of conventional search engines and bibliographic databases, which oftentimes rely on rudimentary keyword-based queries and manual categorization to unearth pertinent articles, thereby faltering in capturing the nuanced semantics, contextual intricacies, and interrelationships embedded within scientific discourse.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAgainst this backdrop of challenges, the emergence of GPT heralds a paradigmatic shift in the modus operandi of researchers vis-\u0026agrave;-vis R\u0026amp;D publications. Conceived by OpenAI, GPT is ensconced within the pantheon of transformer-based models that have precipitated a sea change within the realm of NLP in recent epochs. Diverging from the conventional modus operandi of search engines or bibliographic databases predicated upon predetermined rules or human-curated metadata, GPT imbibes a comprehension of textual semantics and context through unsupervised learning from copious amounts of unlabeled data. This endows GPT with the capacity to apprehend subtle nuances, extrapolate implicit relationships, and engender cogent responses, thus rendering it eminently suitable for tasks necessitating natural language comprehension and generation.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDespite the strides made in text-mining instrumentation, lingering trepidation pervades the scientific community owing to concerns regarding their acuity, precision, and exigencies in temporal commitment. Nonetheless, recent ameliorations in validation metrics have evinced sanguine outcomes, manifesting sensitivity rates of up to ***, specificity rates of up to ***, precision rates of up to ***, and accuracy rates of up to *** across sundry applications. A concomitant reduction in workload time, a pivotal determinant in R\u0026amp;D decision-making, has been realized, with appreciable reductions ranging from *** to ***, thereby assuaging the exigency for expeditious literature review processes.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAutomated text-mining instruments, such as Stanford Core NLP, Apache Open NLP, and the Gensim python package, proffer efficient remedies for data extraction and analysis from research publications. These instruments, conjoined with sophisticated models like BART and methodologies such as zero-shot classification, augur the potential to transmute literature review processes by enabling expeditious, accurate, and precise extraction of research locales and subtopic clustering from geographically contingent research corpus.\u003c/span\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBaseline Comparison: The efficacy of Generative Pre-trained Transformers (GPT) is juxtaposed against conventional methods commonly employed in literature review, encompassing manual scrutiny by human researchers and keyword-based exploration utilizing orthodox search engines.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eEvaluation Metrics: The efficacy of GPT is scrutinized based on a panoply of pivotal metrics, encompassing precision, recall, F1 score, and efficiency in terms of temporal requisites for consummating the review process.\u003c/span\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eExperimental Procedure\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eData Preprocessing: The dataset undergoes a regimen of preprocessing to excise extraneous noise, standardize formatting, and annotate metadata such as title, abstract, keywords, and citation particulars.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBaseline Method Implementation: Human researchers undertake a manual review of a subset of articles culled from the dataset, winnowing key intelligence and fashioning summaries employing conventional methodologies. Concurrently, keyword-based searches are executed employing prevalent search engines to cull pertinent articles predicated upon preordained queries.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGPT Implementation: GPT is subjected to fine-tuning on the preprocessed dataset via transfer learning techniques, thereby tailoring the model to the exigencies of literature review. The fine-tuned model is subsequently deployed to execute automated literature searches, distill key intelligence, and proffer synopses of research articles.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eEvaluation: The efficacy of GPT is adjudged based on its acumen in retrieving pertinent articles, extracting precise intelligence, and proffering cogent summaries vis-\u0026agrave;-vis the baseline methodologies. Additionally, the temporal requisites for consummating the review process via GPT are juxtaposed against those of manual review and keyword-based exploration.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGeoparsing Validation\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe geoparser efficaciously ascertained a research locale (i.e., US state) for *** of the Childhood Elevated Blood Lead Levels (EBLL) corpus, whereas manual data extraction attained a *** success rate in pinpointing research locales. A comparative analysis between the geoparser and manual labeling modalities divulged that the former annotated ** articles, marginally fewer than the ** articles annotated manually. The discordance of *** articles emanated from false negatives, wherein the geoparser faltered in labeling one article each from Illinois and Pennsylvania, which the manual process adeptly annotated. Validation metrics, including sensitivity, precision, and accuracy scores, were meticulously computed, yielding *** true positives, *** true negatives, ** false positives, and *** false negatives. The geoparsing process evinced an accuracy of ***, precision of ***, an F1-Measure of ***, and a sensitivity of ***.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGeographical Distribution\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBar charts delineating the distribution of articles culled by manual and geoparser methodologies unveiled noteworthy congruities and disparities. The quintet of top geographies by article frequency in the validation data comprised New York, Michigan, California, Louisiana, and Massachusetts. Conversely, the geoparser-extracted data spotlighted Michigan, California, Massachusetts, New York, and New Jersey as the foremost geographies by article frequency. The geoparser data supplanted Louisiana to the *** position while elevating New Jersey to the *** position. Geographies devoid of article counts remained consistent between the validation and geoparser-extracted datasets. Subsequent analysis validated the utilization of the geoparser-extracted data for regression analysis by dint of robust validation metrics.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSubtopic Clustering\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSubtopic clustering was exclusively executed for articles featuring an extracted geography. The validation dataset enshrined environmental subtopic articles in *** of articles with an extracted geography. However, the subtopic clustering methodology yielded proportions of *** and *** for environmental subtopic articles in titles and abstracts, respectively. Validation metrics for title subtopic clustering yielded an accuracy of ***, precision of ***, an F1-Measure of ***, and a sensitivity of ***. Correspondingly, for abstract subtopic clustering, metrics encompassed *** accuracy, *** precision, *** F1-Measure, and *** sensitivity. Given the attenuated precision of title subtopic clustering, subsequent analysis favored abstract subtopic clustering.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRegression Analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBaseline Ordinary Least Squares (OLS) regression models were juxtaposed between validation and geoparser-extracted data. The geoparser model evinced enhancement over fallacious and imprecise models, aligning most closely with the geographic data. Quadratic effect models and models featuring subtopic indicator variables were also scrutinized, with the latter evincing robust generalizability across geographies. Analysis of variance (ANOVA) validated notable disparities between the chosen models.\u003c/span\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eModel Selection and Visualization\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCorrelation analysis of fitted values corroborated elevated correlation coefficients (\u0026gt;***) for all models, indicative of a pronounced deviation from random chance. The geoparser model incorporating subtopic and time indicator variables was anointed as the definitive model for visual comparisons. Fitted values were cartographically represented to elucidate EBLL R\u0026amp;D output across disparate geographies, showcasing close concordance between the validation data model and the ultimate geoparser model, with conspicuous divergences in specific states ascribed to binning selection.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe preceding discourse delves into the transformative potential of Generative Pre-trained Transformers (GPT) in revolutionizing literature review processes within the expansive domain of research and development (R\u0026amp;D). Through a comprehensive analysis of the challenges encountered in conventional literature review methodologies and the promising capabilities of GPT in addressing these challenges, several key points emerge for discussion and contemplation.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFirstly, the discussion centers on the efficacy of GPT in mitigating the formidable obstacles posed by the voluminous and intricate nature of scholarly publications. Traditional literature review approaches, reliant on manual scrutiny and keyword-based searches, often struggle to navigate the vast corpus of research literature efficiently. In contrast, GPT, leveraging its advanced natural language processing capabilities, demonstrates the potential to streamline the review process by comprehensively understanding textual semantics and context, thus enabling researchers to extract pertinent information more effectively.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurthermore, the discussion explores the implications of GPT's integration with other text-mining instruments and advanced models such as BART. By harnessing the synergistic potential of these technologies, researchers can unlock new avenues for accelerating literature review processes and gaining deeper insights from research publications. Techniques like zero-shot classification offer particularly promising avenues for enhancing the precision and efficiency of information extraction from diverse and complex datasets.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eA critical aspect of the discussion pertains to the validation and evaluation of GPT's performance in comparison to traditional methodologies. While initial reservations within the scientific community regarding the sensitivity, precision, and time commitments associated with automated text-mining tools are understandable, recent advancements in validation metrics paint a compelling picture of GPT's efficacy. The attainment of sensitivity, specificity, precision, and accuracy rates that rival or exceed those of conventional methods, coupled with significant reductions in workload time, underscores the tangible benefits that GPT can offer to researchers in R\u0026amp;D decision-making processes.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMoreover, the discussion delves into the practical applications of GPT in specific tasks such as geoparsing, geographical distribution analysis, subtopic clustering, and regression analysis. Through meticulous validation and comparison against baseline methodologies, GPT demonstrates its reliability and applicability in handling diverse and intricate datasets, thereby bolstering confidence in its utility as a transformative tool for literature review in R\u0026amp;D.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn conclusion, the discourse underscores the pivotal role of GPT in reshaping the landscape of literature review processes within the realm of research and development. As researchers continue to embrace and refine the integration of advanced NLP techniques into their workflows, the future holds great promise for accelerating innovation, elucidating decision-making processes, and advancing knowledge across various domains of R\u0026amp;D. However, further research and development are warranted to explore the full extent of GPT's capabilities and to address any remaining challenges or limitations in its implementation.\u003c/span\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn summation, the advent of Generative Pre-trained Transformers (GPT) signifies a momentous milestone in the evolution of literature review processes within the purview of research and development (R\u0026amp;D). Traditional methodologies of literature review, ensnared by the prodigious volume and intricacy of published material, are undergoing metamorphosis courtesy of the prowess of GPT in natural language processing (NLP). By harnessing unsupervised learning and semantic comprehension, GPT proffers a promising antidote to the vicissitudes faced by researchers in navigating the vast expanse of scholarly publications.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDespite initial reservations within the scientific community pertaining to the sensitivity, precision, and temporal exigencies associated with automated text-mining tools, recent strides in validation metrics have showcased promising outcomes. Notably, GPT has evinced sensitivity, specificity, precision, and accuracy rates commensurate with or surpassing those of traditional methodologies, whilst appreciably curtailing workload time, thereby redressing a pivotal exigency in R\u0026amp;D decision-making processes.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurthermore, the amalgamation of GPT with other text-mining instruments and sophisticated models like BART, conjoined with methodologies such as zero-shot classification, augurs the potential for further upheaval in literature review processes. These advancements facilitate expeditious, accurate, and precise extraction of research locales and subtopic clustering, thereby amplifying researchers' capacity to contextualize their work, identify lacunae in comprehension, and build upon antecedent discoveries.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThrough meticulous evaluation and juxtaposition against baseline methodologies, encompassing manual scrutiny and keyword-based exploration, the efficacy of GPT in literature review has been duly substantiated. Furthermore, validation via geoparsing, geographical distribution analysis, subtopic clustering, and regression analysis underscores the reliability and applicability of GPT in navigating diverse and intricate datasets.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn denouement, the transformative ramifications of GPT in streamlining literature review processes are incontrovertible. As researchers continue to embrace and refine the integration of advanced NLP techniques into their workflows, the horizon teems with promise for expediting innovation, elucidating decision-making processes, and propelling the boundaries of knowledge across sundry domains of research and development.\u003c/span\u003e \u003c/p\u003e "},{"header":"References","content":"\u003cp\u003eAum, S., Choe, S. srBERT: automatic article classification model for systematic review using BERT. Syst Rev 10, 285 (2021).\u0026nbsp;\u003ca href=\"https://doi.org/10.1186/s13643-021-01763-w\"\u003ehttps://doi.org/10.1186/s13643-021-01763-w\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrett M. Baden, Douglas S. Noonan \u0026amp; Rama Mohana R. Turaga (2007) Scales of justice: Is there a geographic bias in environmental equity analysis?, Journal of Environmental Planning and Management, 50:2, 163-185, DOI:\u0026nbsp;\u003ca href=\"https://doi.org/10.1080/09640560601156433\"\u003e10.1080/09640560601156433\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrennan T, Ernst P, Katz J, Roth E. Building an R\u0026amp;D strategy for modern times. McKinsey Global Publishing. 2020 Nov.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFerilli, Stefano \u0026amp; Esposito, Floriana \u0026amp; Grieco, Domenico. (2014). Automatic Learning of Linguistic Resources for Stopword Removal and Stemming from Text. Procedia Computer Science. 38. 10.1016/j.procs.2014.10.019.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eŘehůřek,\u0026nbsp;\u003ca href=\"https://radimrehurek.com/\"\u003eRadim\u003c/a\u003e. \u0026ldquo;Gensim: Topic Modeling for Humans\u0026rdquo;. Dec 21, 2021. \u0026nbsp;\u003ca href=\"https://radimrehurek.com/gensim/\"\u003ehttps://radimrehurek.com/gensim/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ewa\u003c/p\u003e\n\u003cp\u003eGrokhowsky, Nicholas. (2023). An inferential spatiotemporal approach for knowledge synthesis to identify trends in public health research. (Manuscript submitted for publication)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnopf, Jeffrey. (2006). Doing a Literature Review. PS: Political Science \u0026amp; Politics. 39. 127 - 132. 10.1017/S1049096506060264.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., \u0026amp; Zettlemoyer, L. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Annual Meeting of the Association for Computational Linguistics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarshall, Iain J., Wallace, Byron, 2019, \u0026lsquo;Toward systematic review automation: a practical guide to using machine learning tools in research synthesis\u0026rsquo;, BioMed Central, vol. 8, no. 163\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMillard, Joseph \u0026amp; Freeman, Robin \u0026amp; Newbold, Tim. (2019). Text‐analysis reveals taxonomic and geographic disparities in animal pollination literature. Ecography. 43. 10.1111/ecog.04532.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNanavati, Jay, and Yogesh Ghodasara. \u0026quot;A Comparative Study of Stanford NLP and Apache Open NLP in the view of POS Tagging.\u0026quot; International Journal of Soft Computing and Engineering 5.5 (2015): 57-60.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOliver MN, Matthews KA, Siadaty M, Hauck FR, Pickle LW. Geographic bias related to geocoding in epidemiologic studies. Int J Health Geogr. 2005 Nov 10;4:29. doi: 10.1186/1476-072X-4-29. PMID: 16281976; PMCID: PMC1298322.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePham, B., Jovanovic, J., Bagheri, E. et al. Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow. Syst Rev 10, 156 (2021).\u0026nbsp;\u003ca href=\"https://doi.org/10.1186/s13643-021-01700-x\"\u003ehttps://doi.org/10.1186/s13643-021-01700-x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSarica S, Luo J (2021) Stopwords in technical language processing. PLoS ONE 16(8): e0254937.\u0026nbsp;\u003ca href=\"https://doi.org/10.1371/journal.pone.0254937\"\u003ehttps://doi.org/10.1371/journal.pone.0254937\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSkopec M, Issa H, Reed J, Harris M. The role of geographic bias in knowledge diffusion: a systematic review and narrative synthesis. Res Integr Peer Rev. 2020 Jan 15;5:2. doi: 10.1186/s41073-019-0088-0. PMID: 31956434; PMCID: PMC6961296.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSrivastav A, Singh S. Proposed Model for Context Topic Identification of English and Hindi News Article Through LDA Approach with NLP Technique. J. Inst. Eng. India Ser. B. 2022;103(2):591\u0026ndash;7. doi: 10.1007/s40031-021-00655-w. Epub 2021 Aug 14. PMCID: PMC8363495.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZun-Guang Guo, Gui-Quan Sun, Zhen Wang, Zhen Jin, Li Li, Can Li, Spatial dynamics of an epidemic model with nonlocal infection, Applied Mathematics and Computation, Volume 377, 2020, 125158, ISSN 0096-3003,\u0026nbsp;\u003ca href=\"https://doi.org/10.1016/j.amc.2020.125158\"\u003ehttps://doi.org/10.1016/j.amc.2020.125158\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdina Williams, Nikita Nangia, and Samuel Bowman. 2018.\u0026nbsp;\u003ca href=\"https://aclanthology.org/N18-1101\"\u003eA Broad-Coverage Challenge Corpus for Sentence Understanding through Inference\u003c/a\u003e. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112\u0026ndash;1122, New Orleans, Louisiana. Association for Computational Linguistics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWargo, E. The Mechanics of Choice. Association for Psychological Science. 2011 Dec 28. Cover Story.\u003ca href=\"https://www.psychologicalscience.org/observer/the-mechanics-of-choice\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"https://www.psychologicalscience.org/observer/the-mechanics-of-choice\"\u003ehttps://www.psychologicalscience.org/observer/the-mechanics-of-choice\u003c/a\u003e [accessed 30 August 2022]\u003c/p\u003e\n\u003cp\u003eYin, W., Hay, J., \u0026amp; Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. ArXiv, abs/1909.00161.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYu, Peiyang, Victor Y. Cui, and Jiaxin Guan. \u0026quot;Text classification by using natural language processing.\u0026quot; Journal of Physics: Conference Series. Vol. 1802. No. 4. IOP Publishing, 2021.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"text-mining, rapid review, decision making, topic clustering, research location","lastPublishedDoi":"10.21203/rs.3.rs-3129370/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3129370/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGPT, or Generative Pre-trained Transformer, revolutionizes the efficiency of reviewing research and development (R\u0026amp;D) publications by leveraging its natural language processing capabilities. Traditional methods of literature review are time-consuming and labor-intensive, often requiring researchers to sift through vast amounts of text to extract relevant information. GPT streamlines this process through its ability to comprehend, analyze, and summarize text swiftly.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, GPT can quickly scan through a plethora of publications, identifying key concepts, methodologies, and findings. Its advanced language understanding enables it to grasp complex scientific language and extract essential information accurately. This rapid processing significantly reduces the time researchers spend on manual literature review.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eSecondly, GPT generates concise summaries of research articles, condensing lengthy texts into digestible snippets without sacrificing critical details. These summaries offer researchers a quick overview of the content, allowing them to prioritize articles based on relevance to their own work.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, GPT can assist in identifying connections between different studies and trends within a particular field. By analyzing large volumes of literature, it can detect patterns, emerging topics, and gaps in knowledge, guiding researchers towards fruitful areas for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, GPT can aid in writing literature reviews by suggesting structured outlines and integrating relevant information seamlessly. This not only saves time but also enhances the quality and coherence of the review.\u003c/p\u003e\n\u003cp\u003eOverall, GPT's ability to expedite the process of reviewing R\u0026amp;D publications is invaluable in accelerating the pace of scientific discovery and innovation. By automating tedious tasks and providing insightful analyses, GPT empowers researchers to focus their efforts on advancing knowledge and solving pressing challenges.\u003c/p\u003e","manuscriptTitle":"Harnessing GPT for Efficient Review of Research and Development Publications","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-03-07 18:56:36","doi":"10.21203/rs.3.rs-3129370/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2023-10-11 06:42:25","doi":"10.21203/rs.3.rs-3129370/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b681ad29-342f-4d8b-9c23-dd2c12516453","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-06T12:49:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 18:56:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3129370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3129370","identity":"rs-3129370","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}