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AID-SLR: A Generative Artificial Intelligence-Driven Automated System for Systematic Literature Review
lee, k.; Datta, S.; Paek, H.; Rastegar-Mojarad, M.; Huang, L.-C.; He, L.; Wang, S.; Wang, J.; Wang, X.
2024-07-04
oncology
10.1101/2024.07.03.24309897
medRxiv
Show abstract
Withdrawal StatementThe authors have withdrawn their manuscript owing to [Internal Revision]. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Matching journals
●Non-profit
◐University press
○Commercial
The top 7 journals account for 50% of the predicted probability mass.
2
BMJ Open
●
601 papers in training set
Top 2%
10.1%
Similar papers in this journal
- Agreeability testing of AMSTAR-PF, a tool for quality appraisal of systematic reviews of prognostic factor studies 94%
- Protocol for the development of a tool (INSPECT-SR) to identify problematic randomised controlled trials in systematic reviews of health interventions 94%
- GPT for RCTs?: Using AI to measure adherence to reporting guidelines 93%
3
PLOS ONE
●
5266 papers in training set
Top 18%
9.9%
Similar papers in this journal
- Modelling the impact of behavioural interventions during pandemics: A systematic review 93%
- COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study 92%
- Common misconceptions held by health researchers when interpreting linear regression assumptions, a cross-sectional study 92%
4
PeerJ
◐
308 papers in training set
Top 0.2%
8.1%
Similar papers in this journal
5
Journal of Clinical Epidemiology
○
31 papers in training set
Top 0.2%
4.4%
Similar papers in this journal
- Large language models for conducting systematic reviews: on the rise, but not yet ready for use – a scoping review 94%
- COVID-19 L·OVE repository is highly comprehensive and can be used as a single source for COVID-19 studies 93%
- The impact of retracted randomised controlled trials on systematic reviews and clinical practice guidelines: a meta-epidemiological study 92%
50% of probability mass above
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.