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Attention Score in Context
Title |
Zero‐ and few‐shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials
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Published in |
Research Synthesis Methods, August 2024
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DOI | 10.1002/jrsm.1749 |
Pubmed ID | |
Authors |
Simon Šuster, Timothy Baldwin, Karin Verspoor |
Attention Score in Context
This research output has an Altmetric Attention Score of 83. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2024.
All research outputs
#545,938
of 26,545,486 outputs
Outputs from Research Synthesis Methods
#7
of 579 outputs
Outputs of similar age
#3,281
of 147,429 outputs
Outputs of similar age from Research Synthesis Methods
#1
of 11 outputs
Altmetric has tracked 26,545,486 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 579 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 147,429 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.