ArticleArXiv2023
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation.
Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Large Language Models as Inadvertent Models of Dementia with Lewy Bodies: How a Disorder of Reality Construction Illuminates AI Hallucination.Integrative psychological & behavioral science · 2026Article
Corrections and comments
- Updated by
Authors and funding
16 authors.
Funding
Abstract
Objective: This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. Materials and Methods: We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 24 established benchmarks. Additionally, for the question-answering task, we conducted manual reviews with clinicians, focusing on Readability, Relevancy, Accuracy, and Completeness, to provide users with a more reliable evaluation. Results: The fine-tuned models consistently improved text generation tasks. For instance, it improved the machine translation task by 20.27 in terms of BLEU score. For the answer generation task, manual reviews showed the generated answers had average scores of 4.95 (out of 5), 4.43, 3.9, and 3.31 in Readability, Relevancy, Accuracy, and Completeness, respectively. Conclusions: This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. Ascle offers an all-in-one solution including four advanced generative functions: question-answering, text summarization, text simplification, and machine translation. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/Ascle.
Indexed as
Identifiers
41031083PMC12478431What 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.