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With the proliferation of open-sourced Large Language Models (LLMs) and efficient finetuning techniques, we are on the cusp of the emergence of numerous domain-specific LLMs that have been finetuned for expertise across specialized fields and applications for which the current general-purpose LLMs are unsuitable.
Improving the quality of reports of meta-analyses of randomised controlled trials: the quorom statement
D. Moher, D. J. Cook, S. Eastwood, I. Olkin, D. Rennie, and D. F. Stroup · 1999
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Meta-analysis of observational studies in epidemiology: a proposal for reporting
D. F. Stroup, J. A. Berlin, S. C. Morton, I. Olkin, G. D. Williamson, D. Rennie, D. Moher, B. J. Becker, T. A. Sipe, S. B. Thacker, et al · 2000
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Preferred reporting items for systematic reviews and meta-analyses: the prisma statement
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman · 2009
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Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap
J. H. Elliott, T. Turner, O. Clavisi, J. Thomas, J. P. Higgins, C. Mavergames, and R. L. Gruen · 2014
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Systematic review automation technologies
G. Tsafnat, P. Glasziou, M. K. Choong, A. Dunn, F. Galgani, and E. Coiera · 2014
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The prisma extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations
B. Hutton, G. Salanti, D. M. Caldwell, A. Chaimani, C. H. Schmid, C. Cameron, J. P. Ioannidis, S. Straus, K. Thorlund, et al · 2015
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Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement
D. Moher, L. Shamseer, M. Clarke, D. Ghersi, A. Liberati, M. Petticrew, P. Shekelle, and L. A. Stewart · 2015
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Preferred reporting items for systematic review and meta-analyses of individual participant data: the prisma-ipd statement
L. A. Stewart, M. Clarke, M. Rovers, R. D. Riley, M. Simmonds, G. Stewart, and J. F. Tierney · 2015
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Scientific literature: Information overload
E. Landhuis · 2016
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Robis: a new tool to assess risk of bias in systematic reviews was developed
P. Whiting, J. Savović, J. P. Higgins, D. M. Caldwell, B. C. Reeves, B. Shea, P. Davies, J. Kleijnen, R. Churchill, et al · 2016
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Prisma harms checklist: improving harms reporting in systematic reviews
L. Zorzela, Y. K. Loke, J. P. Ioannidis, S. Golder, P. Santaguida, D. G. Altman, D. Moher, S. Vohra, et al · 2016
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Amstar 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both
B. J. Shea, B. C. Reeves, G. Wells, M. Thuku, C. Hamel, J. Moran, D. Moher, P. Tugwell, V. Welch, E. Kristjansson, et al · 2017
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Developing prisma-rr, a reporting guideline for rapid reviews of primary studies (protocol)
A. Stevens, C. Garritty, M. Hersi, and D. Moher · 2018
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Prisma extension for scoping reviews (prisma-scr): checklist and explanation
A. C. Tricco, E. Lillie, W. Zarin, K. K. O’Brien, H. Colquhoun, D. Levac, D. Moher, M. D. Peters, T. Horsley, L. Weeks, et al · 2018
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Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
I. J. Marshall and B. C. Wallace · 2019
Cited alongside, same era.
A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?
A. M. O’Connor, G. Tsafnat, J. Thomas, P. Glasziou, S. B. Gilbert, and B. Hutton · 2019
Cited alongside, same era.
Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Lora: Low-rank adaptation of large language models, 2021
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
Cited alongside, same era.
The prisma 2020 statement: an updated guideline for reporting systematic reviews
M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, et al · 2021
Qlora: Efficient finetuning of quantized llms, 2023
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer · 2023
Closest in time.
The use of text-mining software to facilitate screening of literature on centredness in health care
E. Forsgren, S. Wallström, C. Feldthusen, N. Zechner, R. Sawatzky, and J. Öhlén · 2023
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A. Gui, J. Ye, and H. Xiao · 2023
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Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models
Z. Hu, Y. Lan, L. Wang, W. Xu, E.-P. Lim, R. K.-W. Lee, L. Bing, and S. Poria · 2023
Closest in time.
Ensemble of deep learning language models to support the creation of living systematic reviews for the covid-19 literature
J. Knafou, Q. Haas, N. Borissov, M. Counotte, N. Low, H. Imeri, A. M. Ipekci, D. Buitrago-Garcia, L. Heron, P. Amini, et al · 2023
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Cited alongside, same era.
How to properly use the prisma statement
R. Sarkis-Onofre, F. Catalá-López, E. Aromataris, and C. Lockwood · 2021
Cited alongside, same era.
Societal biases in language generation: Progress and challenges
E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng · 2021
Cited alongside, same era.
Large language models encode clinical knowledge
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, et al · 2022
Cited alongside, same era.
Adamix: Mixture-of-adaptations for parameter-efficient model tuning
Y. Wang, S. Agarwal, S. Mukherjee, X. Liu, J. Gao, A. H. Awadallah, and J. Gao · 2022
Cited alongside, same era.
https://openai.com/about/
OpenAI: About · 2023
Cited alongside, same era.
Stanford crfm introduces pubmedgpt 2.7b
E. Bolton, D. Hall, M. Yasunaga, T. Lee, C. Manning, and P. Liang · 2023
Cited alongside, same era.
Parameter-efficient fine-tuning design spaces
J. Chen, A. Zhang, X. Shi, M. Li, A. Smola, and D. Yang · 2023
Cited alongside, same era.
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Do we still need clinical language models?
E. Lehman, E. Hernandez, D. Mahajan, J. Wulff, M. J. Smith, Z. Ziegler, D. Nadler, P. Szolovits, A. Johnson, and E. Alsentzer · 2023
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The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study
A. E. Muller, R. C. Berg, J. F. Meneses-Echavez, H. M. Ames, T. C. Borge, P. S. J. Jardim, C. Cooper, and C. J. Rose · 2023
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Are chatgpt and large language models “the answer” to bringing us closer to systematic review automation?
R. Qureshi, D. Shaughnessy, K. A. Gill, K. A. Robinson, T. Li, and E. Agai · 2023
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Towards expert-level medical question answering with large language models
K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, L. Hou, K. Clark, S. Pfohl, H. Cole-Lewis, D. Neal, et al · 2023
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Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
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Bloomberggpt: A large language model for finance
S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann · 2023
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Lima: Less is more for alignment
C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, et al · 2023
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Guidance to best tools and practices for systematic reviews
K. Kolaski, L. R. Logan, and J. P. A. Ioannidis · 2046
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