ArticleScientific reports2025
A case study using sewage metagenomic data for assessment of text-to-SQL capabilities in large language models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Relational databases offer an efficient solution for storing and retrieving complex data sets, yet the requirement for SQL programming expertise presents a significant challenge for many life science users. We explore whether a cutting-edge large language model can effectively translate plain English queries into SQL scripts (Text-to-SQL), thereby simplifying database interaction and eliminating the typical usage barriers. A complex database comprising 19 interconnected tables of metagenomic analyses from 239 sewage samples across five European cities was available. A large language model was provided with details of the database's structure and background information on its contents. We evaluated the functionalities of this "SewageGPT" tool and assessed the accuracy of its responses to complex questions and visualisation of results. Providing a detailed description of the database enabled SewageGPT to accurately respond to complex inquiries, accelerating the database querying process. Knowledge of the database content proved beneficial, as it minimized the risk of ambiguities in queries; however, ambiguities can lead to incorrect responses. Therefore, human oversight remains crucial, particularly for questions that lack detail or involve ambiguities. The integration of state-of-the-art large language models with direct database connectivity substantially enhances the efficiency of query generation, statistical analysis and visualization of the results.
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.