ArticleScientific reports2025
Prompt engineering in ChatGPT for literature review: practical guide exemplified with studies on white phosphors.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Decoding Clinician Authorial Style: A Style-Informed Pipeline for Clinical Document Summary Generation with Large Language Models.Research square · 2026Article
- SpeciMate: Improving metadata extraction from digitised biological specimens.Biodiversity data journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recent advancements in large language models (LLMs) such as ChatGPT have been transforming the ways we approach science tasks, including data analysis, experimental design, writing, and literature review. However, due to the lack of specialized knowledge and inherent issues such as plagiarism and hallucinations (i.e., false or misleading outputs), it is necessary for users to verify the output information. To address these issues, prompt engineering has become a significant task. In this study, we evaluate the performance of different prompt styles for extracting information from literature abstracts and emphasize the importance of prompt engineering for such scientific tasks. The literature on white phosphor materials is used for this study due to the availability of important and quantitative information in the abstracts. Through detailed comparative and quantitative evaluation, we provide guidance on preparing suitable and effective prompts based on the types of information sought.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.